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[RLlib] Move learning_starts logic from buffers into training_step(). (#26032)

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Artur Niederfahrenhorst 2022-08-11 13:07:30 +02:00 • committed by GitHub
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commit 0dceddb912
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82 changed files with 437 additions and 331 deletions
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2
release/long_running_tests/workloads/apex.py
View file

@ -45,8 +45,8 @@ run_experiments(
"num_gpus": 0,
"replay_buffer_config": {
"capacity": 10000,
"learning_starts": 0,
},
"num_steps_sampled_before_learning_starts": 0,
"rollout_fragment_length": 1,
"train_batch_size": 1,
"min_iter_time_s": 10,

2
release/rllib_tests/learning_tests/yaml_files/ddpg/ddpg-hopperbulletenv-v0.yaml
View file

@ -30,8 +30,8 @@ ddpg-hopperbulletenv-v0:
prioritized_replay_alpha: 0.6
prioritized_replay_beta: 0.4
prioritized_replay_eps: 0.000001
learning_starts: 500
worker_side_prioritization: false
num_steps_sampled_before_learning_starts: 500
clip_rewards: false
actor_lr: 0.001
critic_lr: 0.001

2
release/rllib_tests/learning_tests/yaml_files/dqn/dqn-breakoutnoframeskip-v4.yaml
View file

@ -20,8 +20,8 @@ dqn-breakoutnoframeskip-v4:
replay_buffer_config:
type: MultiAgentReplayBuffer
capacity: 1000000
learning_starts: 20000
prioritized_replay_alpha: 0.5
num_steps_sampled_before_learning_starts: 20000
rollout_fragment_length: 4
train_batch_size: 32
exploration_config:

2
release/rllib_tests/learning_tests/yaml_files/sac/sac-halfcheetahbulletenv-v0.yaml
View file

@ -25,8 +25,8 @@ sac-halfcheetahbulletenv-v0:
target_network_update_freq: 1
min_sample_timesteps_per_iteration: 1000
replay_buffer_config:
learning_starts: 10000
type: MultiAgentPrioritizedReplayBuffer
num_steps_sampled_before_learning_starts: 10000
optimization:
actor_learning_rate: 0.0003
critic_learning_rate: 0.0003

3
release/rllib_tests/learning_tests/yaml_files/td3/td3-halfcheetahbulletenv-v0.yaml
View file

@ -9,7 +9,6 @@ td3-halfcheetahbulletenv-v0:
time_total_s: 3600
config:
num_gpus: 1
replay_buffer_config:
learning_starts: 10000
num_steps_sampled_before_learning_starts: 10000
exploration_config:
random_timesteps: 10000

8
rllib/BUILD
View file

@ -1215,7 +1215,7 @@ py_test(
"--env", "Pendulum-v1",
"--run", "APEX_DDPG",
"--stop", "'{\"training_iteration\": 1}'",
"--config", "'{\"framework\": \"tf\", \"num_workers\": 2, \"optimizer\": {\"num_replay_buffer_shards\": 1}, \"replay_buffer_config\": {\"learning_starts\": 100}, \"min_time_s_per_iteration\": 1, \"batch_mode\": \"complete_episodes\"}'",
"--config", "'{\"framework\": \"tf\", \"num_workers\": 2, \"optimizer\": {\"num_replay_buffer_shards\": 1}, \"num_steps_sampled_before_learning_starts\": 100, \"min_time_s_per_iteration\": 1, \"batch_mode\": \"complete_episodes\"}'",
"--ray-num-cpus", "4",
]
)
@ -1272,7 +1272,7 @@ py_test(
"--env", "CartPole-v0",
"--run", "DQN",
"--stop", "'{\"training_iteration\": 1}'",
"--config", "'{\"framework\": \"tf\", \"input\": \"tests/data/cartpole\", \"replay_buffer_config\": {\"learning_starts\": 0}, \"off_policy_estimation_methods\": {\"wis\": {\"type\": \"wis\"}, \"is\": {\"type\": \"is\"}}, \"exploration_config\": {\"type\": \"SoftQ\"}}'"
"--config", "'{\"framework\": \"tf\", \"input\": \"tests/data/cartpole\", \"num_steps_sampled_before_learning_starts\": 0, \"off_policy_estimation_methods\": {\"wis\": {\"type\": \"wis\"}, \"is\": {\"type\": \"is\"}}, \"exploration_config\": {\"type\": \"SoftQ\"}}'"
]
)
@ -1284,7 +1284,7 @@ py_test(
"--env", "PongDeterministic-v4",
"--run", "DQN",
"--stop", "'{\"training_iteration\": 1}'",
"--config", "'{\"framework\": \"tf\", \"lr\": 1e-4, \"exploration_config\": {\"epsilon_timesteps\": 200000, \"final_epsilon\": 0.01}, \"replay_buffer_config\": {\"capacity\": 10000, \"learning_starts\": 10000}, \"rollout_fragment_length\": 4, \"target_network_update_freq\": 1000, \"gamma\": 0.99}'"
"--config", "'{\"framework\": \"tf\", \"lr\": 1e-4, \"exploration_config\": {\"epsilon_timesteps\": 200000, \"final_epsilon\": 0.01}, \"replay_buffer_config\": {\"capacity\": 10000}, \"num_steps_sampled_before_learning_starts\": 10000, \"rollout_fragment_length\": 4, \"target_network_update_freq\": 1000, \"gamma\": 0.99}'"
]
)
@ -3159,7 +3159,7 @@ py_test(
tags = ["team:rllib", "exclusive", "examples", "examples_R"],
size = "large",
srcs = ["examples/recommender_system_with_recsim_and_slateq.py"],
args = ["--stop-iters=2", "--learning-starts=100", "--framework=tf2", "--use-tune", "--random-test-episodes=10", "--env-num-candidates=50", "--env-slate-size=2"],
args = ["--stop-iters=2", "--num-steps-sampled-before-learning_starts=100", "--framework=tf2", "--use-tune", "--random-test-episodes=10", "--env-num-candidates=50", "--env-slate-size=2"],
)
py_test(

26
rllib/algorithms/alpha_zero/alpha_zero.py
View file

@ -105,17 +105,18 @@ class AlphaZeroConfig(AlgorithmConfig):
self.sgd_minibatch_size = 128
self.shuffle_sequences = True
self.num_sgd_iter = 30
self.learning_starts = 1000
self.replay_buffer_config = {
"type": "ReplayBuffer",
# Size of the replay buffer in batches (not timesteps!).
"capacity": 1000,
# When to start returning samples (in batches, not timesteps!).
"learning_starts": 500,
# Choosing `fragments` here makes it so that the buffer stores entire
# batches, instead of sequences, episodes or timesteps.
"storage_unit": "fragments",
}
# Number of timesteps to collect from rollout workers before we start
# sampling from replay buffers for learning. Whether we count this in agent
# steps or environment steps depends on config["multiagent"]["count_steps_by"].
self.num_steps_sampled_before_learning_starts = 1000
self.lr_schedule = None
self.vf_share_layers = False
self.mcts_config = {
@ -169,6 +170,7 @@ class AlphaZeroConfig(AlgorithmConfig):
vf_share_layers: Optional[bool] = None,
mcts_config: Optional[dict] = None,
ranked_rewards: Optional[dict] = None,
num_steps_sampled_before_learning_starts: Optional[int] = None,
**kwargs,
) -> "AlphaZeroConfig":
"""Sets the training related configuration.
@ -221,6 +223,10 @@ class AlphaZeroConfig(AlgorithmConfig):
mcts_config: MCTS specific settings.
ranked_rewards: Settings for the ranked reward (r2) algorithm
from: https://arxiv.org/pdf/1807.01672.pdf
num_steps_sampled_before_learning_starts: Number of timesteps to collect
from rollout workers before we start sampling from replay buffers for
learning. Whether we count this in agent steps or environment steps
depends on config["multiagent"]["count_steps_by"].
Returns:
This updated AlgorithmConfig object.
@ -244,6 +250,10 @@ class AlphaZeroConfig(AlgorithmConfig):
self.mcts_config = mcts_config
if ranked_rewards is not None:
self.ranked_rewards = ranked_rewards
if num_steps_sampled_before_learning_starts is not None:
self.num_steps_sampled_before_learning_starts = (
num_steps_sampled_before_learning_starts
)
return self
@ -344,9 +354,19 @@ class AlphaZero(Algorithm):
self.local_replay_buffer.add(batch)
if self.local_replay_buffer is not None:
# Update target network every `target_network_update_freq` sample steps.
cur_ts = self._counters[
NUM_AGENT_STEPS_SAMPLED
if self._by_agent_steps
else NUM_ENV_STEPS_SAMPLED
]
if cur_ts > self.config["num_steps_sampled_before_learning_starts"]:
train_batch = self.local_replay_buffer.sample(
self.config["train_batch_size"]
)
else:
train_batch = None
else:
train_batch = concat_samples(new_sample_batches)

6
rllib/algorithms/apex_ddpg/apex_ddpg.py
View file

@ -82,8 +82,6 @@ class ApexDDPGConfig(DDPGConfig):
"prioritized_replay_beta": 0.4,
# Epsilon to add to the TD errors when updating priorities.
"prioritized_replay_eps": 1e-6,
# How many steps of the model to sample before learning starts.
"learning_starts": 50000,
# Whether all shards of the replay buffer must be co-located
# with the learner process (running the execution plan).
# This is preferred b/c the learner process should have quick
@ -99,6 +97,10 @@ class ApexDDPGConfig(DDPGConfig):
# prioritization, for example: MultiAgentPrioritizedReplayBuffer.
"prioritized_replay": DEPRECATED_VALUE,
}
# Number of timesteps to collect from rollout workers before we start
# sampling from replay buffers for learning. Whether we count this in agent
# steps or environment steps depends on config["multiagent"]["count_steps_by"].
self.num_steps_sampled_before_learning_starts = 50000
self.target_network_update_freq = 500000
self.training_intensity = 1
# __sphinx_doc_end__

2
rllib/algorithms/apex_ddpg/tests/test_apex_ddpg.py
View file

@ -25,7 +25,7 @@ class TestApexDDPG(unittest.TestCase):
.rollouts(num_rollout_workers=2)
.reporting(min_sample_timesteps_per_iteration=100)
.training(
replay_buffer_config={"learning_starts": 0},
num_steps_sampled_before_learning_starts=0,
optimizer={"num_replay_buffer_shards": 1},
)
.environment(env="Pendulum-v1")

13
rllib/algorithms/apex_dqn/apex_dqn.py
View file

@ -132,6 +132,10 @@ class ApexDQNConfig(DQNConfig):
self.train_batch_size = 512
self.target_network_update_freq = 500000
self.training_intensity = 1
# Number of timesteps to collect from rollout workers before we start
# sampling from replay buffers for learning. Whether we count this in agent
# steps or environment steps depends on config["multiagent"]["count_steps_by"].
self.num_steps_sampled_before_learning_starts = 50000
# max number of inflight requests to each sampling worker
# see the AsyncRequestsManager class for more details
@ -161,7 +165,6 @@ class ApexDQNConfig(DQNConfig):
"prioritized_replay_beta": 0.4,
# Epsilon to add to the TD errors when updating priorities.
"prioritized_replay_eps": 1e-6,
"learning_starts": 50000,
# Whether all shards of the replay buffer must be co-located
# with the learner process (running the execution plan).
# This is preferred b/c the learner process should have quick
@ -241,7 +244,6 @@ class ApexDQNConfig(DQNConfig):
{
"_enable_replay_buffer_api": True,
"type": "MultiAgentReplayBuffer",
"learning_starts": 1000,
"capacity": 50000,
"replay_batch_size": 32,
"replay_sequence_length": 1,
@ -441,6 +443,13 @@ class ApexDQN(DQN):
# only do this if there are remote workers (config["num_workers"] > 1)
if self.workers.remote_workers():
self.update_workers(worker_samples_collected)
# Update target network every `target_network_update_freq` sample steps.
cur_ts = self._counters[
NUM_AGENT_STEPS_SAMPLED if self._by_agent_steps else NUM_ENV_STEPS_SAMPLED
]
if cur_ts > self.config["num_steps_sampled_before_learning_starts"]:
# trigger a sample from the replay actors and enqueue operation to the
# learner thread.
self.sample_from_replay_buffer_place_on_learner_queue_non_blocking(

12
rllib/algorithms/apex_dqn/tests/test_apex_dqn.py
View file

@ -26,9 +26,7 @@ class TestApexDQN(unittest.TestCase):
.rollouts(num_rollout_workers=0)
.resources(num_gpus=0)
.training(
replay_buffer_config={
"learning_starts": 1000,
},
num_steps_sampled_before_learning_starts=0,
optimizer={
"num_replay_buffer_shards": 1,
},
@ -53,9 +51,7 @@ class TestApexDQN(unittest.TestCase):
.rollouts(num_rollout_workers=3)
.resources(num_gpus=0)
.training(
replay_buffer_config={
"learning_starts": 1000,
},
num_steps_sampled_before_learning_starts=0,
optimizer={
"num_replay_buffer_shards": 1,
},
@ -110,7 +106,6 @@ class TestApexDQN(unittest.TestCase):
replay_buffer_config={
"no_local_replay_buffer": True,
"type": "MultiAgentPrioritizedReplayBuffer",
"learning_starts": 10,
"capacity": 100,
"prioritized_replay_alpha": 0.6,
# Beta parameter for sampling from prioritized replay buffer.
@ -121,6 +116,9 @@ class TestApexDQN(unittest.TestCase):
# Initial lr, doesn't really matter because of the schedule below.
lr=0.2,
lr_schedule=[[0, 0.2], [100, 0.001]],
# Number of timesteps to collect from rollout workers before we start
# sampling from replay buffers for learning.
num_steps_sampled_before_learning_starts=10,
)
.reporting(
min_sample_timesteps_per_iteration=10,

2
rllib/algorithms/cql/tests/test_cql.py
View file

@ -55,7 +55,7 @@ class TestCQL(unittest.TestCase):
clip_actions=False,
train_batch_size=2000,
twin_q=True,
replay_buffer_config={"learning_starts": 0},
num_steps_sampled_before_learning_starts=0,
bc_iters=2,
)
.evaluation(

6
rllib/algorithms/ddpg/ddpg.py
View file

@ -107,8 +107,6 @@ class DDPGConfig(SimpleQConfig):
"prioritized_replay_beta": 0.4,
# Epsilon to add to the TD errors when updating priorities.
"prioritized_replay_eps": 1e-6,
# How many steps of the model to sample before learning starts.
"learning_starts": 1500,
# Whether to compute priorities on workers.
"worker_side_prioritization": False,
}
@ -117,6 +115,10 @@ class DDPGConfig(SimpleQConfig):
self.grad_clip = None
self.train_batch_size = 256
self.target_network_update_freq = 0
# Number of timesteps to collect from rollout workers before we start
# sampling from replay buffers for learning. Whether we count this in agent
# steps or environment steps depends on config["multiagent"]["count_steps_by"].
self.num_steps_sampled_before_learning_starts = 1500
# .rollouts()
self.rollout_fragment_length = 1

20
rllib/algorithms/ddpg/tests/test_ddpg.py
View file

@ -34,10 +34,11 @@ class TestDDPG(unittest.TestCase):
def test_ddpg_compilation(self):
"""Test whether DDPG can be built with both frameworks."""
config = ddpg.DDPGConfig()
config.num_workers = 0
config.num_envs_per_worker = 2
config.replay_buffer_config["learning_starts"] = 0
config = (
ddpg.DDPGConfig()
.training(num_steps_sampled_before_learning_starts=0)
.rollouts(num_rollout_workers=0, num_envs_per_worker=2)
)
explore = config.exploration_config.update({"random_timesteps": 100})
config.exploration(exploration_config=explore)
@ -63,7 +64,12 @@ class TestDDPG(unittest.TestCase):
def test_ddpg_exploration_and_with_random_prerun(self):
"""Tests DDPG's Exploration (w/ random actions for n timesteps)."""
core_config = ddpg.DDPGConfig().rollouts(num_rollout_workers=0)
core_config = (
ddpg.DDPGConfig()
.rollouts(num_rollout_workers=0)
.training(num_steps_sampled_before_learning_starts=0)
)
obs = np.array([0.0, 0.1, -0.1])
# Test against all frameworks.
@ -125,7 +131,8 @@ class TestDDPG(unittest.TestCase):
def test_ddpg_loss_function(self):
"""Tests DDPG loss function results across all frameworks."""
config = ddpg.DDPGConfig()
config = ddpg.DDPGConfig().training(num_steps_sampled_before_learning_starts=0)
# Run locally.
config.seed = 42
config.num_workers = 0
@ -138,7 +145,6 @@ class TestDDPG(unittest.TestCase):
config.replay_buffer_config = {
"type": "MultiAgentReplayBuffer",
"capacity": 50000,
"learning_starts": 0,
}
# Use very simple nets.
config.actor_hiddens = [10]

18
rllib/algorithms/dqn/dqn.py
View file

@ -217,7 +217,6 @@ class DQNConfig(SimpleQConfig):
{
"_enable_replay_buffer_api": True,
"type": "MultiAgentReplayBuffer",
"learning_starts": 1000,
"capacity": 50000,
"replay_sequence_length": 1,
}
@ -370,6 +369,12 @@ class DQN(SimpleQ):
"timestep": self._counters[NUM_ENV_STEPS_SAMPLED],
}
# Update target network every `target_network_update_freq` sample steps.
cur_ts = self._counters[
NUM_AGENT_STEPS_SAMPLED if self._by_agent_steps else NUM_ENV_STEPS_SAMPLED
]
if cur_ts > self.config["num_steps_sampled_before_learning_starts"]:
for _ in range(sample_and_train_weight):
# Sample training batch (MultiAgentBatch) from replay buffer.
train_batch = sample_min_n_steps_from_buffer(
@ -378,11 +383,6 @@ class DQN(SimpleQ):
count_by_agent_steps=self._by_agent_steps,
)
# Old-style replay buffers return None if learning has not started
if train_batch is None or len(train_batch) == 0:
self.workers.local_worker().set_global_vars(global_vars)
break
# Postprocess batch before we learn on it
post_fn = self.config.get("before_learn_on_batch") or (lambda b, *a: b)
train_batch = post_fn(train_batch, self.workers, self.config)
@ -407,12 +407,6 @@ class DQN(SimpleQ):
train_results,
)
# Update target network every `target_network_update_freq` sample steps.
cur_ts = self._counters[
NUM_AGENT_STEPS_SAMPLED
if self._by_agent_steps
else NUM_ENV_STEPS_SAMPLED
]
last_update = self._counters[LAST_TARGET_UPDATE_TS]
if cur_ts - last_update >= self.config["target_network_update_freq"]:
to_update = self.workers.local_worker().get_policies_to_train()

9
rllib/algorithms/dqn/tests/test_dqn.py
View file

@ -24,7 +24,11 @@ class TestDQN(unittest.TestCase):
def test_dqn_compilation(self):
"""Test whether DQN can be built on all frameworks."""
num_iterations = 1
config = dqn.dqn.DQNConfig().rollouts(num_rollout_workers=2)
config = (
dqn.dqn.DQNConfig()
.rollouts(num_rollout_workers=2)
.training(num_steps_sampled_before_learning_starts=0)
)
for _ in framework_iterator(config, with_eager_tracing=True):
# Double-dueling DQN.
@ -60,7 +64,8 @@ class TestDQN(unittest.TestCase):
dqn.dqn.DQNConfig()
.rollouts(num_rollout_workers=0)
.environment(env_config={"is_slippery": False, "map_name": "4x4"})
)
).training(num_steps_sampled_before_learning_starts=0)
obs = np.array(0)
# Test against all frameworks.

30
rllib/algorithms/dreamer/dreamer.py
View file

@ -106,6 +106,10 @@ class DreamerConfig(AlgorithmConfig):
# .training()
self.gamma = 0.99
# Number of timesteps to collect from rollout workers before we start
# sampling from replay buffers for learning. Whether we count this in agent
# steps or environment steps depends on config["multiagent"]["count_steps_by"].
self.num_steps_sampled_before_learning_starts = 0
# .environment()
self.env_config = {
@ -134,6 +138,7 @@ class DreamerConfig(AlgorithmConfig):
prefill_timesteps: Optional[int] = None,
explore_noise: Optional[float] = None,
dreamer_model: Optional[dict] = None,
num_steps_sampled_before_learning_starts: Optional[int] = None,
**kwargs,
) -> "DreamerConfig":
"""
@ -153,6 +158,10 @@ class DreamerConfig(AlgorithmConfig):
prefill_timesteps: Prefill timesteps.
explore_noise: Exploration Gaussian noise.
dreamer_model: Custom model config.
num_steps_sampled_before_learning_starts: Number of timesteps to collect
from rollout workers before we start sampling from replay buffers for
learning. Whether we count this in agent steps or environment steps
depends on config["multiagent"]["count_steps_by"].
Returns:
@ -189,6 +198,10 @@ class DreamerConfig(AlgorithmConfig):
self.explore_noise = explore_noise
if dreamer_model is not None:
self.dreamer_model = dreamer_model
if num_steps_sampled_before_learning_starts is not None:
self.num_steps_sampled_before_learning_starts = (
num_steps_sampled_before_learning_starts
)
return self
@ -245,13 +258,20 @@ class DreamerIteration:
self.batch_size = batch_size
def __call__(self, samples):
# Update target network every `target_network_update_freq` sample steps.
cur_ts = self._counters[
NUM_AGENT_STEPS_SAMPLED if self._by_agent_steps else NUM_ENV_STEPS_SAMPLED
]
if cur_ts > self.config["num_steps_sampled_before_learning_starts"]:
# Dreamer training loop.
for n in range(self.dreamer_train_iters):
print(f"sub-iteration={n}/{self.dreamer_train_iters}")
batch = self.episode_buffer.sample(self.batch_size)
# if n == self.dreamer_train_iters - 1:
# batch["log_gif"] = True
fetches = self.worker.learn_on_batch(batch)
else:
fetches = {}
# Custom Logging
policy_fetches = fetches[DEFAULT_POLICY_ID]["learner_stats"]
@ -378,6 +398,12 @@ class Dreamer(Algorithm):
fetches = {}
# Update target network every `target_network_update_freq` sample steps.
cur_ts = self._counters[
NUM_AGENT_STEPS_SAMPLED if self._by_agent_steps else NUM_ENV_STEPS_SAMPLED
]
if cur_ts > self.config["num_steps_sampled_before_learning_starts"]:
# Dreamer training loop.
# Run multiple sub-iterations for each training iteration.
for n in range(dreamer_train_iters):

7
rllib/algorithms/maddpg/maddpg.py
View file

@ -84,12 +84,14 @@ class MADDPGConfig(AlgorithmConfig):
# prioritization, for example: MultiAgentPrioritizedReplayBuffer.
"prioritized_replay": DEPRECATED_VALUE,
"capacity": int(1e6),
# How many steps of the model to sample before learning starts.
"learning_starts": 1024 * 25,
# Force lockstep replay mode for MADDPG.
"replay_mode": "lockstep",
}
self.training_intensity = None
# Number of timesteps to collect from rollout workers before we start
# sampling from replay buffers for learning. Whether we count this in agent
# steps or environment steps depends on config["multiagent"]["count_steps_by"].
self.num_steps_sampled_before_learning_starts = 1024 * 25
self.critic_lr = 1e-2
self.actor_lr = 1e-2
self.target_network_update_freq = 0
@ -157,7 +159,6 @@ class MADDPGConfig(AlgorithmConfig):
{
"_enable_replay_buffer_api": True,
"type": "MultiAgentReplayBuffer",
"learning_starts": 1000,
"capacity": 50000,
"replay_sequence_length": 1,
}

32
rllib/algorithms/qmix/qmix.py
View file

@ -79,17 +79,20 @@ class QMixConfig(SimpleQConfig):
self.lr = 0.0005
self.train_batch_size = 32
self.target_network_update_freq = 500
# Number of timesteps to collect from rollout workers before we start
# sampling from replay buffers for learning. Whether we count this in agent
# steps or environment steps depends on config["multiagent"]["count_steps_by"].
self.num_steps_sampled_before_learning_starts = 1000
self.replay_buffer_config = {
"type": "ReplayBuffer",
# Specify prioritized replay by supplying a buffer type that supports
# prioritization, for example: MultiAgentPrioritizedReplayBuffer.
"prioritized_replay": DEPRECATED_VALUE,
# Size of the replay buffer in batches
# Size of the replay buffer in batches (not timesteps!).
"capacity": 1000,
# Choosing `fragments` here makes it so that the buffer stores entire
# batches, instead of sequences, episodes or timesteps.
"storage_unit": "fragments",
"learning_starts": 1000,
# Whether to compute priorities on workers.
"worker_side_prioritization": False,
}
@ -255,6 +258,14 @@ class QMix(SimpleQ):
# Store new samples in the replay buffer.
self.local_replay_buffer.add(batch)
# Update target network every `target_network_update_freq` sample steps.
cur_ts = self._counters[
NUM_AGENT_STEPS_SAMPLED if self._by_agent_steps else NUM_ENV_STEPS_SAMPLED
]
train_results = {}
if cur_ts > self.config["num_steps_sampled_before_learning_starts"]:
# Sample n batches from replay buffer until the total number of timesteps
# reaches `train_batch_size`.
train_batch = sample_min_n_steps_from_buffer(
@ -262,8 +273,6 @@ class QMix(SimpleQ):
min_steps=self.config["train_batch_size"],
count_by_agent_steps=self._by_agent_steps,
)
if train_batch is None:
return {}
# Learn on the training batch.
# Use simple optimizer (only for multi-agent or tf-eager; all other
@ -273,15 +282,7 @@ class QMix(SimpleQ):
else:
train_results = multi_gpu_train_one_step(self, train_batch)
# TODO: Move training steps counter update outside of `train_one_step()` method.
# # Update train step counters.
# self._counters[NUM_ENV_STEPS_TRAINED] += train_batch.env_steps()
# self._counters[NUM_AGENT_STEPS_TRAINED] += train_batch.agent_steps()
# Update target network every `target_network_update_freq` sample steps.
cur_ts = self._counters[
NUM_AGENT_STEPS_SAMPLED if self._by_agent_steps else NUM_ENV_STEPS_SAMPLED
]
last_update = self._counters[LAST_TARGET_UPDATE_TS]
if cur_ts - last_update >= self.config["target_network_update_freq"]:
to_update = self.workers.local_worker().get_policies_to_train()
@ -295,12 +296,13 @@ class QMix(SimpleQ):
self.local_replay_buffer, self.config, train_batch, train_results
)
# Update weights and global_vars - after learning on the local worker - on all
# remote workers.
# Update weights and global_vars - after learning on the local worker -
# on all remote workers.
global_vars = {
"timestep": self._counters[NUM_ENV_STEPS_SAMPLED],
}
# Update remote workers' weights and global vars after learning on local worker.
# Update remote workers' weights and global vars after learning on local
# worker.
with self._timers[SYNCH_WORKER_WEIGHTS_TIMER]:
self.workers.sync_weights(global_vars=global_vars)

1
rllib/algorithms/r2d2/tests/test_r2d2.py
View file

@ -70,6 +70,7 @@ class TestR2D2(unittest.TestCase):
lr=5e-4,
zero_init_states=True,
replay_buffer_config={"replay_burn_in": 20},
num_steps_sampled_before_learning_starts=0,
)
.exploration(exploration_config={"epsilon_timesteps": 100000})
)

12
rllib/algorithms/sac/sac.py
View file

@ -64,8 +64,6 @@ class SACConfig(AlgorithmConfig):
"_enable_replay_buffer_api": True,
"type": "MultiAgentPrioritizedReplayBuffer",
"capacity": int(1e6),
# How many steps of the model to sample before learning starts.
"learning_starts": 1500,
# If True prioritized replay buffer will be used.
"prioritized_replay": False,
"prioritized_replay_alpha": 0.6,
@ -90,6 +88,10 @@ class SACConfig(AlgorithmConfig):
# .training()
self.train_batch_size = 256
# Number of timesteps to collect from rollout workers before we start
# sampling from replay buffers for learning. Whether we count this in agent
# steps or environment steps depends on config["multiagent"]["count_steps_by"].
self.num_steps_sampled_before_learning_starts = 1500
# .reporting()
self.min_time_s_per_iteration = 1
@ -123,6 +125,7 @@ class SACConfig(AlgorithmConfig):
target_network_update_freq: Optional[int] = None,
_deterministic_loss: Optional[bool] = None,
_use_beta_distribution: Optional[bool] = None,
num_steps_sampled_before_learning_starts: Optional[int] = None,
**kwargs,
) -> "SACConfig":
"""Sets the training related configuration.
@ -166,7 +169,6 @@ class SACConfig(AlgorithmConfig):
{
"_enable_replay_buffer_api": True,
"type": "MultiAgentReplayBuffer",
"learning_starts": 1000,
"capacity": 50000,
"replay_batch_size": 32,
"replay_sequence_length": 1,
@ -267,6 +269,10 @@ class SACConfig(AlgorithmConfig):
self._deterministic_loss = _deterministic_loss
if _use_beta_distribution is not None:
self._use_beta_distribution = _use_beta_distribution
if num_steps_sampled_before_learning_starts is not None:
self.num_steps_sampled_before_learning_starts = (
num_steps_sampled_before_learning_starts
)
return self

1
rllib/algorithms/sac/tests/test_rnnsac.py
View file

@ -46,6 +46,7 @@ class TestRNNSAC(unittest.TestCase):
"zero_init_states": True,
},
lr=5e-4,
num_steps_sampled_before_learning_starts=0,
)
)
num_iterations = 1

12
rllib/algorithms/sac/tests/test_sac.py
View file

@ -79,7 +79,10 @@ class TestSAC(unittest.TestCase):
.training(
n_step=3,
twin_q=True,
replay_buffer_config={"learning_starts": 0, "capacity": 40000},
replay_buffer_config={
"capacity": 40000,
},
num_steps_sampled_before_learning_starts=0,
store_buffer_in_checkpoints=True,
train_batch_size=10,
)
@ -172,7 +175,7 @@ class TestSAC(unittest.TestCase):
_deterministic_loss=True,
q_model_config={"fcnet_hiddens": [10]},
policy_model_config={"fcnet_hiddens": [10]},
replay_buffer_config={"learning_starts": 0},
num_steps_sampled_before_learning_starts=0,
)
.rollouts(num_rollout_workers=0)
.reporting(
@ -523,7 +526,10 @@ class TestSAC(unittest.TestCase):
config = (
sac.SACConfig()
.training(
replay_buffer_config={"learning_starts": 0, "capacity": 10},
replay_buffer_config={
"capacity": 10,
},
num_steps_sampled_before_learning_starts=0,
train_batch_size=5,
)
.rollouts(

42
rllib/algorithms/simple_q/simple_q.py
View file

@ -82,7 +82,6 @@ class SimpleQConfig(AlgorithmConfig):
>>> })
>>> config = SimpleQConfig().rollouts(rollout_fragment_length=32)\
>>> .exploration(exploration_config=explore_config)\
>>> .training(learning_starts=200)
Example:
>>> from ray.rllib.algorithms.simple_q import SimpleQConfig
@ -106,14 +105,16 @@ class SimpleQConfig(AlgorithmConfig):
# __sphinx_doc_begin__
self.target_network_update_freq = 500
self.replay_buffer_config = {
# How many steps of the model to sample before learning starts.
"learning_starts": 1000,
"type": "MultiAgentReplayBuffer",
"capacity": 50000,
# The number of contiguous environment steps to replay at once. This
# may be set to greater than 1 to support recurrent models.
"replay_sequence_length": 1,
}
# Number of timesteps to collect from rollout workers before we start
# sampling from replay buffers for learning. Whether we count this in agent
# steps or environment steps depends on config["multiagent"]["count_steps_by"].
self.num_steps_sampled_before_learning_starts = 1000
self.store_buffer_in_checkpoints = False
self.lr_schedule = None
self.adam_epsilon = 1e-8
@ -166,6 +167,7 @@ class SimpleQConfig(AlgorithmConfig):
lr_schedule: Optional[List[List[Union[int, float]]]] = None,
adam_epsilon: Optional[float] = None,
grad_clip: Optional[int] = None,
num_steps_sampled_before_learning_starts: Optional[int] = None,
**kwargs,
) -> "SimpleQConfig":
"""Sets the training related configuration.
@ -180,7 +182,6 @@ class SimpleQConfig(AlgorithmConfig):
{
"_enable_replay_buffer_api": True,
"type": "MultiAgentReplayBuffer",
"learning_starts": 1000,
"capacity": 50000,
"replay_sequence_length": 1,
}
@ -221,6 +222,10 @@ class SimpleQConfig(AlgorithmConfig):
timestep 0.
adam_epsilon: Adam optimizer's epsilon hyper parameter.
grad_clip: If not None, clip gradients during optimization at this value.
num_steps_sampled_before_learning_starts: Number of timesteps to collect
from rollout workers before we start sampling from replay buffers for
learning. Whether we count this in agent steps or environment steps
depends on config["multiagent"]["count_steps_by"].
Returns:
This updated AlgorithmConfig object.
@ -249,6 +254,10 @@ class SimpleQConfig(AlgorithmConfig):
self.adam_epsilon = adam_epsilon
if grad_clip is not None:
self.grad_clip = grad_clip
if num_steps_sampled_before_learning_starts is not None:
self.num_steps_sampled_before_learning_starts = (
num_steps_sampled_before_learning_starts
)
return self
@ -341,14 +350,14 @@ class SimpleQ(Algorithm):
global_vars = {
"timestep": self._counters[NUM_ENV_STEPS_SAMPLED],
}
# Update target network every `target_network_update_freq` sample steps.
cur_ts = self._counters[
NUM_AGENT_STEPS_SAMPLED if self._by_agent_steps else NUM_ENV_STEPS_SAMPLED
]
if cur_ts > self.config["num_steps_sampled_before_learning_starts"]:
# Use deprecated replay() to support old replay buffers for now
train_batch = self.local_replay_buffer.sample(batch_size)
# If not yet learning, early-out here and do not perform learning, weight-
# synching, or target net updating.
if train_batch is None or len(train_batch) == 0:
self.workers.local_worker().set_global_vars(global_vars)
return {}
# Learn on the training batch.
# Use simple optimizer (only for multi-agent or tf-eager; all other
@ -366,15 +375,6 @@ class SimpleQ(Algorithm):
train_results,
)
# TODO: Move training steps counter update outside of `train_one_step()` method.
# # Update train step counters.
# self._counters[NUM_ENV_STEPS_TRAINED] += train_batch.env_steps()
# self._counters[NUM_AGENT_STEPS_TRAINED] += train_batch.agent_steps()
# Update target network every `target_network_update_freq` sample steps.
cur_ts = self._counters[
NUM_AGENT_STEPS_SAMPLED if self._by_agent_steps else NUM_ENV_STEPS_SAMPLED
]
last_update = self._counters[LAST_TARGET_UPDATE_TS]
if cur_ts - last_update >= self.config["target_network_update_freq"]:
with self._timers[TARGET_NET_UPDATE_TIMER]:
@ -385,10 +385,12 @@ class SimpleQ(Algorithm):
self._counters[NUM_TARGET_UPDATES] += 1
self._counters[LAST_TARGET_UPDATE_TS] = cur_ts
# Update weights and global_vars - after learning on the local worker - on all
# remote workers.
# Update weights and global_vars - after learning on the local worker -
# on all remote workers.
with self._timers[SYNCH_WORKER_WEIGHTS_TIMER]:
self.workers.sync_weights(global_vars=global_vars)
else:
train_results = {}
# Return all collected metrics for the iteration.
return train_results

9
rllib/algorithms/simple_q/tests/test_simple_q.py
View file

@ -31,8 +31,10 @@ class TestSimpleQ(unittest.TestCase):
def test_simple_q_compilation(self):
"""Test whether SimpleQ can be built on all frameworks."""
# Run locally and with compression
config = simple_q.SimpleQConfig().rollouts(
num_rollout_workers=0, compress_observations=True
config = (
simple_q.SimpleQConfig()
.rollouts(num_rollout_workers=0, compress_observations=True)
.training(num_steps_sampled_before_learning_starts=0)
)
num_iterations = 2
@ -57,7 +59,8 @@ class TestSimpleQ(unittest.TestCase):
model={
"fcnet_hiddens": [10],
"fcnet_activation": "linear",
}
},
num_steps_sampled_before_learning_starts=0,
)
for fw in framework_iterator(config):

11
rllib/algorithms/slateq/slateq.py
View file

@ -90,9 +90,11 @@ class SlateQConfig(AlgorithmConfig):
"replay_sequence_length": 1,
# Whether to compute priorities on workers.
"worker_side_prioritization": False,
# How many steps of the model to sample before learning starts.
"learning_starts": 20000,
}
# Number of timesteps to collect from rollout workers before we start
# sampling from replay buffers for learning. Whether we count this in agent
# steps or environment steps depends on config["multiagent"]["count_steps_by"].
self.num_steps_sampled_before_learning_starts = 20000
# Override some of AlgorithmConfig's default values with SlateQ-specific values.
self.exploration_config = {
@ -139,6 +141,7 @@ class SlateQConfig(AlgorithmConfig):
rmsprop_epsilon: Optional[float] = None,
grad_clip: Optional[float] = None,
n_step: Optional[int] = None,
num_steps_sampled_before_learning_starts: Optional[int] = None,
**kwargs,
) -> "SlateQConfig":
"""Sets the training related configuration.
@ -202,6 +205,10 @@ class SlateQConfig(AlgorithmConfig):
self.grad_clip = grad_clip
if n_step is not None:
self.n_step = n_step
if num_steps_sampled_before_learning_starts is not None:
self.num_steps_sampled_before_learning_starts = (
num_steps_sampled_before_learning_starts
)
return self

2
rllib/algorithms/slateq/tests/test_slateq.py
View file

@ -26,7 +26,7 @@ class TestSlateQ(unittest.TestCase):
config = (
slateq.SlateQConfig()
.environment(env=InterestEvolutionRecSimEnv)
.training(replay_buffer_config={"learning_starts": 1000})
.training(num_steps_sampled_before_learning_starts=1000)
)
num_iterations = 1

5
rllib/algorithms/td3/td3.py
View file

@ -71,9 +71,12 @@ class TD3Config(DDPGConfig):
# prioritization, for example: MultiAgentPrioritizedReplayBuffer.
"prioritized_replay": DEPRECATED_VALUE,
"capacity": 1000000,
"learning_starts": 10000,
"worker_side_prioritization": False,
}
# Number of timesteps to collect from rollout workers before we start
# sampling from replay buffers for learning. Whether we count this in agent
# steps or environment steps depends on config["multiagent"]["count_steps_by"].
self.num_steps_sampled_before_learning_starts = 10000
# .exploration()
# TD3 uses Gaussian Noise by default.

2
rllib/algorithms/tests/test_worker_failures.py
View file

@ -231,7 +231,7 @@ class TestWorkerFailure(unittest.TestCase):
"min_sample_timesteps_per_iteration": 1000,
"min_time_s_per_iteration": 1,
"explore": False,
"learning_starts": 1000,
"num_steps_sampled_before_learning_starts": 1000,
"target_network_update_freq": 100,
"optimizer": {
"num_replay_buffer_shards": 1,

2
rllib/examples/custom_input_api.py
View file

@ -95,7 +95,7 @@ if __name__ == "__main__":
"clip_actions": True,
"twin_q": True,
"train_batch_size": 2000,
"learning_starts": 0,
"num_steps_sampled_before_learning_starts": 0,
"bc_iters": 100,
"metrics_num_episodes_for_smoothing": 5,
"evaluation_interval": 1,

2
rllib/examples/custom_keras_model.py
View file

@ -116,7 +116,7 @@ if __name__ == "__main__":
assert r["model"]["foo"] == 42, result
if args.run == "DQN":
extra_config = {"replay_buffer_config": {"learning_starts": 0}}
extra_config = {"num_steps_sampled_before_learning_starts": 0}
else:
extra_config = {}

2
rllib/examples/multi_agent_parameter_sharing.py
View file

@ -27,10 +27,10 @@ if __name__ == "__main__":
"num_workers": 2,
"num_envs_per_worker": 8,
"replay_buffer_config": {
"learning_starts": 1000,
"capacity": int(1e5),
"prioritized_replay_alpha": 0.5,
},
"num_steps_sampled_before_learning_starts": 1000,
"compress_observations": True,
"rollout_fragment_length": 20,
"train_batch_size": 512,

2
rllib/examples/offline_rl.py
View file

@ -38,7 +38,7 @@ if __name__ == "__main__":
config["bc_iters"] = 0
config["clip_actions"] = False
config["normalize_actions"] = True
config["replay_buffer_config"]["learning_starts"] = 256
config["num_steps_sampled_before_learning_starts"] = 256
config["rollout_fragment_length"] = 1
# Test without prioritized replay
config["replay_buffer_config"]["type"] = "MultiAgentReplayBuffer"

15
rllib/examples/recommender_system_with_recsim_and_slateq.py
View file

@ -45,7 +45,7 @@ parser.add_argument(
choices=["interest-evolution", "interest-exploration", "long-term-satisfaction"],
help=("Select the RecSim env to use."),
)
parser.add_argument("--learning-starts", type=int, default=20000)
parser.add_argument(
"--random-test-episodes",
type=int,
@ -71,6 +71,15 @@ parser.add_argument(
"`--env-slate-size` from each timestep. These candidates will be "
"sampled by the environment's built-in document sampler model.",
)
parser.add_argument(
"--num-steps-sampled-before-learning_starts",
type=int,
default=20000,
help="Number of timesteps to collect from rollout workers before we start "
"sampling from replay buffers for learning..",
)
parser.add_argument(
"--env-slate-size",
type=int,
@ -126,9 +135,7 @@ def main():
"num_gpus": args.num_gpus,
"num_workers": args.num_workers,
"env_config": env_config,
"replay_buffer_config": {
"learning_starts": args.learning_starts,
},
"num_steps_sampled_before_learning_starts": args.num_steps_sampled_before_learning_starts, # noqa E501
}
# Perform a test run on the env with a random agent to see, what

2
rllib/examples/rnnsac_stateless_cartpole.py
View file

@ -24,9 +24,9 @@ param_space = {
"type": "MultiAgentReplayBuffer",
"storage_unit": "sequences",
"capacity": 100000,
"learning_starts": 1000,
"replay_burn_in": 4,
},
"num_steps_sampled_before_learning_starts": 1000,
"train_batch_size": 480,
"target_network_update_freq": 480,
"tau": 0.3,

2
rllib/examples/serving/cartpole_server.py
View file

@ -180,7 +180,7 @@ if __name__ == "__main__":
# Example of using DQN (supports off-policy actions).
config.update(
{
"replay_buffer_config": {"learning_starts": 100},
"num_steps_sampled_before_learning_starts": 100,
"min_sample_timesteps_per_iteration": 200,
"n_step": 3,
"rollout_fragment_length": 4,

2
rllib/examples/two_step_game.py
View file

@ -115,7 +115,7 @@ if __name__ == "__main__":
"env_config": {
"actions_are_logits": True,
},
"replay_buffer_config": {"learning_starts": 100},
"num_steps_sampled_before_learning_starts": 100,
"multiagent": {
"policies": {
"pol1": PolicySpec(

8
rllib/examples/two_trainer_workflow.py
View file

@ -83,9 +83,7 @@ class MyAlgo(Algorithm):
# Call super's `setup` to create rollout workers.
super().setup(config)
# Create local replay buffer.
self.local_replay_buffer = MultiAgentReplayBuffer(
num_shards=1, learning_starts=1000, capacity=50000
)
self.local_replay_buffer = MultiAgentReplayBuffer(num_shards=1, capacity=50000)
@override(Algorithm)
def training_step(self) -> ResultDict:
@ -93,6 +91,7 @@ class MyAlgo(Algorithm):
# into replay buffer.
ppo_batches = []
num_env_steps = 0
# PPO batch size fixed at 200.
while num_env_steps < 200:
ma_batches = synchronous_parallel_sample(
@ -112,8 +111,9 @@ class MyAlgo(Algorithm):
# DQN sub-flow.
dqn_train_results = {}
if self._counters[NUM_ENV_STEPS_SAMPLED] > 1000:
dqn_train_batch = self.local_replay_buffer.sample(num_items=64)
if dqn_train_batch is not None:
dqn_train_results = train_one_step(self, dqn_train_batch, ["dqn_policy"])
self._counters["agent_steps_trained_DQN"] += dqn_train_batch.agent_steps()
print(

5
rllib/tests/test_checkpoint_restore.py
View file

@ -28,6 +28,7 @@ CONFIGS = {
"optimizer": {
"num_replay_buffer_shards": 1,
},
"num_steps_sampled_before_learning_starts": 0,
},
"ARS": {
"explore": False,
@ -39,9 +40,11 @@ CONFIGS = {
"DDPG": {
"explore": False,
"min_sample_timesteps_per_iteration": 100,
"num_steps_sampled_before_learning_starts": 0,
},
"DQN": {
"explore": False,
"num_steps_sampled_before_learning_starts": 0,
},
"ES": {
"explore": False,
@ -59,9 +62,11 @@ CONFIGS = {
},
"SimpleQ": {
"explore": False,
"num_steps_sampled_before_learning_starts": 0,
},
"SAC": {
"explore": False,
"num_steps_sampled_before_learning_starts": 0,
},
}

3
rllib/tests/test_custom_resource.py
View file

@ -22,6 +22,9 @@ def test_custom_resource(algorithm):
"custom_resources_per_worker": {"custom_resource": 0.01},
}
if algorithm == "APEX":
config["num_steps_sampled_before_learning_starts"] = 0
stop = {"training_iteration": 1}
tune.run(

30
rllib/tests/test_eager_support.py
View file

@ -44,12 +44,19 @@ class TestEagerSupportPG(unittest.TestCase):
def test_simple_q(self):
check_support(
"SimpleQ",
{"num_workers": 0, "replay_buffer_config": {"learning_starts": 0}},
{
"num_workers": 0,
"num_steps_sampled_before_learning_starts": 0,
},
)
def test_dqn(self):
check_support(
"DQN", {"num_workers": 0, "replay_buffer_config": {"learning_starts": 0}}
"DQN",
{
"num_workers": 0,
"num_steps_sampled_before_learning_starts": 0,
},
)
def test_ddpg(self):
@ -91,12 +98,19 @@ class TestEagerSupportOffPolicy(unittest.TestCase):
def test_simple_q(self):
check_support(
"SimpleQ",
{"num_workers": 0, "replay_buffer_config": {"learning_starts": 0}},
{
"num_workers": 0,
"replay_buffer_config": {"num_steps_sampled_before_learning_starts": 0},
},
)
def test_dqn(self):
check_support(
"DQN", {"num_workers": 0, "replay_buffer_config": {"learning_starts": 0}}
"DQN",
{
"num_workers": 0,
"num_steps_sampled_before_learning_starts": 0,
},
)
def test_ddpg(self):
@ -113,7 +127,7 @@ class TestEagerSupportOffPolicy(unittest.TestCase):
"APEX",
{
"num_workers": 2,
"replay_buffer_config": {"learning_starts": 0},
"replay_buffer_config": {"num_steps_sampled_before_learning_starts": 0},
"num_gpus": 0,
"min_time_s_per_iteration": 1,
"min_sample_timesteps_per_iteration": 100,
@ -125,7 +139,11 @@ class TestEagerSupportOffPolicy(unittest.TestCase):
def test_sac(self):
check_support(
"SAC", {"num_workers": 0, "replay_buffer_config": {"learning_starts": 0}}
"SAC",
{
"num_workers": 0,
"num_steps_sampled_before_learning_starts": 0,
},
)

7
rllib/tests/test_execution.py
View file

@ -208,20 +208,16 @@ class TestExecution(unittest.TestCase):
def test_store_to_replay_local(self):
buf = MultiAgentReplayBuffer(
num_shards=1,
learning_starts=200,
capacity=1000,
prioritized_replay_alpha=0.6,
prioritized_replay_beta=0.4,
prioritized_replay_eps=0.0001,
)
assert len(buf.sample(100)) == 0
workers = make_workers(0)
a = ParallelRollouts(workers, mode="bulk_sync")
b = a.for_each(StoreToReplayBuffer(local_buffer=buf))
next(b)
assert len(buf.sample(100)) == 0 # learning hasn't started yet
next(b)
assert buf.sample(100).count == 100
@ -232,7 +228,6 @@ class TestExecution(unittest.TestCase):
ReplayActor = ray.remote(num_cpus=0)(MultiAgentReplayBuffer)
actor = ReplayActor.remote(
num_shards=1,
learning_starts=200,
capacity=1000,
prioritized_replay_alpha=0.6,
prioritized_replay_beta=0.4,
@ -244,8 +239,6 @@ class TestExecution(unittest.TestCase):
a = ParallelRollouts(workers, mode="bulk_sync")
b = a.for_each(StoreToReplayBuffer(actors=[actor]))
next(b)
assert len(ray.get(actor.sample.remote(100))) == 0 # learning hasn't started
next(b)
assert ray.get(actor.sample.remote(100)).count == 100

6
rllib/tests/test_supported_multi_agent.py
View file

@ -97,8 +97,8 @@ class TestSupportedMultiAgentOffPolicy(unittest.TestCase):
"num_gpus": 0,
"replay_buffer_config": {
"capacity": 1000,
"learning_starts": 10,
},
"num_steps_sampled_before_learning_starts": 10,
"min_time_s_per_iteration": 1,
"target_network_update_freq": 100,
"optimizer": {
@ -115,8 +115,8 @@ class TestSupportedMultiAgentOffPolicy(unittest.TestCase):
"min_sample_timesteps_per_iteration": 100,
"replay_buffer_config": {
"capacity": 1000,
"learning_starts": 10,
},
"num_steps_sampled_before_learning_starts": 10,
"num_gpus": 0,
"min_time_s_per_iteration": 1,
"target_network_update_freq": 100,
@ -131,8 +131,8 @@ class TestSupportedMultiAgentOffPolicy(unittest.TestCase):
"min_sample_timesteps_per_iteration": 1,
"replay_buffer_config": {
"capacity": 1000,
"learning_starts": 500,
},
"num_steps_sampled_before_learning_starts": 10,
"use_state_preprocessor": True,
},
)

2
rllib/tuned_examples/apex_dqn/cartpole-apex-dqn.yaml
View file

@ -21,7 +21,7 @@ cartpole-apex-dqn-training-itr:
replay_buffer_config:
type: MultiAgentPrioritizedReplayBuffer
capacity: 20000
learning_starts: 1000
num_steps_sampled_before_learning_starts: 1000
num_gpus: 0

2
rllib/tuned_examples/compact-regression-test.yaml
View file

@ -129,8 +129,8 @@ atari-basic-dqn:
noisy: false
replay_buffer_config:
type: MultiAgentReplayBuffer
learning_starts: 20000
capacity: 1000000
num_steps_sampled_before_learning_starts: 20000
n_step: 1
target_network_update_freq: 8000
lr: .0000625

2
rllib/tuned_examples/cql/halfcheetah-bc.yaml
View file

@ -28,7 +28,7 @@ halfcheetah_bc:
rollout_fragment_length: 1
replay_buffer_config:
type: MultiAgentReplayBuffer
learning_starts: 10
num_steps_sampled_before_learning_starts: 10
train_batch_size: 256
target_network_update_freq: 0
min_train_timesteps_per_iteration: 1000

2
rllib/tuned_examples/cql/halfcheetah-cql.yaml
View file

@ -30,7 +30,7 @@ halfcheetah_cql:
rollout_fragment_length: 1
replay_buffer_config:
type: MultiAgentReplayBuffer
learning_starts: 256
num_steps_sampled_before_learning_starts: 256
train_batch_size: 256
target_network_update_freq: 0
min_train_timesteps_per_iteration: 1000

2
rllib/tuned_examples/cql/hopper-bc.yaml
View file

@ -28,7 +28,7 @@ hopper_bc:
rollout_fragment_length: 1
replay_buffer_config:
type: MultiAgentReplayBuffer
learning_starts: 10
num_steps_sampled_before_learning_starts: 10
train_batch_size: 256
target_network_update_freq: 0
min_train_timesteps_per_iteration: 1000

2
rllib/tuned_examples/cql/hopper-cql.yaml
View file

@ -28,7 +28,7 @@ hopper_cql:
rollout_fragment_length: 1
replay_buffer_config:
type: MultiAgentReplayBuffer
learning_starts: 10
num_steps_sampled_before_learning_starts: 10
train_batch_size: 256
target_network_update_freq: 0
min_train_timesteps_per_iteration: 1000

3
rllib/tuned_examples/ddpg/halfcheetah-ddpg.yaml
View file

@ -37,6 +37,8 @@ halfcheetah-ddpg:
prioritized_replay_beta: 0.4
prioritized_replay_eps: 0.000001
worker_side_prioritization: false
num_steps_sampled_before_learning_starts: 500
clip_rewards: False
# === Optimization ===
@ -45,7 +47,6 @@ halfcheetah-ddpg:
use_huber: false
huber_threshold: 1.0
l2_reg: 0.000001
learning_starts: 500
rollout_fragment_length: 1
train_batch_size: 64

2
rllib/tuned_examples/ddpg/halfcheetah-pybullet-ddpg.yaml
View file

@ -29,13 +29,13 @@ ddpg-halfcheetahbulletenv-v0:
prioritized_replay_beta: 0.4
prioritized_replay_eps: 0.000001
worker_side_prioritization: false
num_steps_sampled_before_learning_starts: 500
clip_rewards: false
actor_lr: 0.001
critic_lr: 0.001
use_huber: true
huber_threshold: 1.0
l2_reg: 0.000001
learning_starts: 500
rollout_fragment_length: 1
train_batch_size: 48
num_workers: 0

2
rllib/tuned_examples/ddpg/hopper-pybullet-ddpg.yaml
View file

@ -32,7 +32,7 @@ ddpg-hopperbulletenv-v0:
prioritized_replay_beta: 0.4
prioritized_replay_eps: 0.000001
worker_side_prioritization: false
learning_starts: 500
num_steps_sampled_before_learning_starts: 500
clip_rewards: False
actor_lr: 0.001
critic_lr: 0.001

2
rllib/tuned_examples/ddpg/mountaincarcontinuous-ddpg.yaml
View file

@ -38,6 +38,7 @@ mountaincarcontinuous-ddpg:
prioritized_replay_beta: 0.4
prioritized_replay_eps: 0.000001
worker_side_prioritization: false
num_steps_sampled_before_learning_starts: 1000
clip_rewards: False
# === Optimization ===
@ -46,7 +47,6 @@ mountaincarcontinuous-ddpg:
use_huber: False
huber_threshold: 1.0
l2_reg: 0.00001
learning_starts: 1000
rollout_fragment_length: 1
train_batch_size: 64

2
rllib/tuned_examples/ddpg/pendulum-ddpg-fake-gpus.yaml
View file

@ -20,7 +20,7 @@ pendulum-ddpg-fake-gpus:
type: MultiAgentPrioritizedReplayBuffer
capacity: 10000
worker_side_prioritization: false
learning_starts: 500
num_steps_sampled_before_learning_starts: 500
clip_rewards: false
use_huber: true
train_batch_size: 64

2
rllib/tuned_examples/ddpg/pendulum-ddpg.yaml
View file

@ -37,6 +37,7 @@ pendulum-ddpg:
type: MultiAgentPrioritizedReplayBuffer
capacity: 10000
worker_side_prioritization: false
num_steps_sampled_before_learning_starts: 500
clip_rewards: False
# === Optimization ===
@ -45,7 +46,6 @@ pendulum-ddpg:
use_huber: True
huber_threshold: 1.0
l2_reg: 0.000001
learning_starts: 500
rollout_fragment_length: 1
train_batch_size: 64

2
rllib/tuned_examples/dqn/atari-dist-dqn.yaml
View file

@ -14,7 +14,7 @@ atari-dist-dqn:
replay_buffer_config:
type: MultiAgentReplayBuffer
capacity: 1000000
learning_starts: 20000
num_steps_sampled_before_learning_starts: 20000
n_step: 1
target_network_update_freq: 8000
lr: .0000625

2
rllib/tuned_examples/dqn/atari-dqn.yaml
View file

@ -17,8 +17,8 @@ atari-basic-dqn:
noisy: false
replay_buffer_config:
type: MultiAgentReplayBuffer
learning_starts: 20000
capacity: 1000000
num_steps_sampled_before_learning_starts: 20000
n_step: 1
target_network_update_freq: 8000
lr: .0000625

2
rllib/tuned_examples/dqn/atari-duel-ddqn.yaml
View file

@ -17,8 +17,8 @@ dueling-ddqn:
noisy: false
replay_buffer_config:
type: MultiAgentReplayBuffer
learning_starts: 20000
capacity: 1000000
num_steps_sampled_before_learning_starts: 20000
n_step: 1
target_network_update_freq: 8000
lr: .0000625

2
rllib/tuned_examples/dqn/pong-dqn.yaml
View file

@ -14,7 +14,7 @@ pong-deterministic-dqn:
replay_buffer_config:
type: MultiAgentPrioritizedReplayBuffer
capacity: 50000
learning_starts: 10000
num_steps_sampled_before_learning_starts: 10000
rollout_fragment_length: 4
train_batch_size: 32
exploration_config:

2
rllib/tuned_examples/dqn/pong-rainbow.yaml
View file

@ -18,8 +18,8 @@ pong-deterministic-rainbow:
replay_buffer_config:
type: MultiAgentPrioritizedReplayBuffer
prioritized_replay_alpha: 0.5
learning_starts: 10000
capacity: 50000
num_steps_sampled_before_learning_starts: 10000
n_step: 3
gpu: True
model:

2
rllib/tuned_examples/maddpg/two-step-game-maddpg.yaml
View file

@ -12,7 +12,7 @@ two-step-game-maddpg:
env_config:
actions_are_logits: true
learning_starts: 200
num_steps_sampled_before_learning_starts: 200
multiagent:
policies:

2
rllib/tuned_examples/sac/atari-sac.yaml
View file

@ -34,11 +34,11 @@ atari-sac-tf-and-torch:
type: MultiAgentPrioritizedReplayBuffer
capacity: 1000000
# How many steps of the model to sample before learning starts.
learning_starts: 100000
# If True prioritized replay buffer will be used.
prioritized_replay_alpha: 0.6
prioritized_replay_beta: 0.4
prioritized_replay_eps: 1e-6
num_steps_sampled_before_learning_starts: 100000
train_batch_size: 64
min_sample_timesteps_per_iteration: 4
# Paper uses 20k random timesteps, which is not exactly the same, but

2
rllib/tuned_examples/sac/cartpole-continuous-pybullet-sac.yaml
View file

@ -14,7 +14,7 @@ cartpole-sac:
n_step: 3
replay_buffer_config:
type: MultiAgentPrioritizedReplayBuffer
learning_starts: 256
num_steps_sampled_before_learning_starts: 256
initial_alpha: 0.2
clip_actions: false
min_sample_timesteps_per_iteration: 1000

2
rllib/tuned_examples/sac/halfcheetah-pybullet-sac.yaml
View file

@ -24,7 +24,7 @@ halfcheetah-pybullet-sac:
min_sample_timesteps_per_iteration: 1000
replay_buffer_config:
type: MultiAgentPrioritizedReplayBuffer
learning_starts: 10000
num_steps_sampled_before_learning_starts: 10000
optimization:
actor_learning_rate: 0.0003
critic_learning_rate: 0.0003

2
rllib/tuned_examples/sac/halfcheetah-sac.yaml
View file

@ -25,7 +25,7 @@ halfcheetah_sac:
min_sample_timesteps_per_iteration: 1000
replay_buffer_config:
type: MultiAgentPrioritizedReplayBuffer
learning_starts: 10000
num_steps_sampled_before_learning_starts: 10000
optimization:
actor_learning_rate: 0.0003
critic_learning_rate: 0.0003

2
rllib/tuned_examples/sac/mspacman-sac.yaml
View file

@ -33,7 +33,7 @@ mspacman-sac-tf:
# seems to work nevertheless.
replay_buffer_config:
type: MultiAgentPrioritizedReplayBuffer
learning_starts: 20000
num_steps_sampled_before_learning_starts: 20000
optimization:
actor_learning_rate: 0.0003
critic_learning_rate: 0.0003

2
rllib/tuned_examples/sac/pendulum-sac-fake-gpus.yaml
View file

@ -26,7 +26,7 @@ pendulum-sac-fake-gpus:
min_sample_timesteps_per_iteration: 1000
replay_buffer_config:
type: MultiAgentPrioritizedReplayBuffer
learning_starts: 256
num_steps_sampled_before_learning_starts: 256
num_workers: 0
metrics_smoothing_episodes: 5

2
rllib/tuned_examples/sac/pendulum-sac.yaml
View file

@ -27,7 +27,7 @@ pendulum-sac:
min_sample_timesteps_per_iteration: 1000
replay_buffer_config:
type: MultiAgentPrioritizedReplayBuffer
learning_starts: 256
num_steps_sampled_before_learning_starts: 256
optimization:
actor_learning_rate: 0.0003
critic_learning_rate: 0.0003

2
rllib/tuned_examples/sac/pendulum-transformed-actions-sac.yaml
View file

@ -35,7 +35,7 @@ transformed-actions-pendulum-sac-dummy-torch:
min_sample_timesteps_per_iteration: 1000
replay_buffer_config:
type: MultiAgentPrioritizedReplayBuffer
learning_starts: 256
num_steps_sampled_before_learning_starts: 256
optimization:
actor_learning_rate: 0.0003
critic_learning_rate: 0.0003

2
rllib/tuned_examples/slateq/interest-evolution-10-candidates-recsim-env-slateq-fake-gpus.yaml
View file

@ -35,12 +35,12 @@ interest-evolution-recsim-env-slateq:
replay_buffer_config:
capacity: 100000
num_steps_sampled_before_learning_starts: 10000
# Double learning rate and batch size.
lr: 0.002
train_batch_size: 64
learning_starts: 10000
target_network_update_freq: 3200
metrics_num_episodes_for_smoothing: 200

2
rllib/tuned_examples/slateq/interest-evolution-10-candidates-recsim-env-slateq.yaml
View file

@ -31,10 +31,10 @@ interest-evolution-recsim-env-slateq:
replay_buffer_config:
capacity: 100000
num_steps_sampled_before_learning_starts: 10000
lr: 0.001
learning_starts: 10000
target_network_update_freq: 3200
metrics_num_episodes_for_smoothing: 200

3
rllib/tuned_examples/td3/invertedpendulum-td3.yaml
View file

@ -16,8 +16,7 @@ invertedpendulum-td3:
critic_hiddens: [32, 32]
# === Exploration ===
replay_buffer_config:
learning_starts: 1000
num_steps_sampled_before_learning_starts: 1000
exploration_config:
random_timesteps: 1000

2
rllib/tuned_examples/td3/mujoco-td3.yaml
View file

@ -22,7 +22,7 @@ mujoco-td3:
random_timesteps: 10000
replay_buffer_config:
type: MultiAgentReplayBuffer
learning_starts: 10000
num_steps_sampled_before_learning_starts: 10000
# === Evaluation ===
evaluation_interval: 10
evaluation_duration: 10

2
rllib/tuned_examples/td3/pendulum-td3-fake-gpus.yaml
View file

@ -12,7 +12,7 @@ pendulum-td3-fake-gpus:
replay_buffer_config:
type: MultiAgentReplayBuffer
learning_starts: 5000
num_steps_sampled_before_learning_starts: 5000
exploration_config:
random_timesteps: 5000
evaluation_interval: 10

2
rllib/tuned_examples/td3/pendulum-td3.yaml
View file

@ -14,6 +14,6 @@ pendulum-td3:
# === Exploration ===
replay_buffer_config:
type: MultiAgentReplayBuffer
learning_starts: 5000
num_steps_sampled_before_learning_starts: 5000
exploration_config:
random_timesteps: 5000

8
rllib/utils/replay_buffers/multi_agent_mixin_replay_buffer.py
View file

@ -77,7 +77,6 @@ class MultiAgentMixInReplayBuffer(MultiAgentPrioritizedReplayBuffer):
capacity: int = 10000,
storage_unit: str = "timesteps",
num_shards: int = 1,
learning_starts: int = 1000,
replay_mode: str = "independent",
replay_sequence_override: bool = True,
replay_sequence_length: int = 1,
@ -99,9 +98,6 @@ class MultiAgentMixInReplayBuffer(MultiAgentPrioritizedReplayBuffer):
are stored in episodes, replay_sequence_length is ignored.
num_shards: The number of buffer shards that exist in total
(including this one).
learning_starts: Number of timesteps after which a call to
`replay()` will yield samples (before that, `replay()` will
return None).
replay_mode: One of "independent" or "lockstep". Determines,
whether batches are sampled independently or to an equal
amount.
@ -152,7 +148,6 @@ class MultiAgentMixInReplayBuffer(MultiAgentPrioritizedReplayBuffer):
capacity=capacity,
storage_unit=storage_unit,
num_shards=num_shards,
learning_starts=learning_starts,
replay_mode=replay_mode,
replay_sequence_override=replay_sequence_override,
replay_sequence_length=replay_sequence_length,
@ -266,9 +261,6 @@ class MultiAgentMixInReplayBuffer(MultiAgentPrioritizedReplayBuffer):
# Merge kwargs, overwriting standard call arguments
kwargs = merge_dicts_with_warning(self.underlying_buffer_call_args, kwargs)
if self._num_added < self.replay_starts:
return MultiAgentBatch({}, 0)
def mix_batches(_policy_id):
"""Mixes old with new samples.

5
rllib/utils/replay_buffers/multi_agent_prioritized_replay_buffer.py
View file

@ -40,7 +40,6 @@ class MultiAgentPrioritizedReplayBuffer(
capacity: int = 10000,
storage_unit: str = "timesteps",
num_shards: int = 1,
learning_starts: int = 1000,
replay_mode: str = "independent",
replay_sequence_override: bool = True,
replay_sequence_length: int = 1,
@ -63,9 +62,6 @@ class MultiAgentPrioritizedReplayBuffer(
ignored.
num_shards: The number of buffer shards that exist in total
(including this one).
learning_starts: Number of timesteps after which a call to
`replay()` will yield samples (before that, `replay()` will
return None).
replay_mode: One of "independent" or "lockstep". Determines,
whether batches are sampled independently or to an equal
amount.
@ -136,7 +132,6 @@ class MultiAgentPrioritizedReplayBuffer(
capacity=shard_capacity,
storage_unit=storage_unit,
replay_sequence_override=replay_sequence_override,
learning_starts=learning_starts,
replay_mode=replay_mode,
replay_sequence_length=replay_sequence_length,
replay_burn_in=replay_burn_in,

7
rllib/utils/replay_buffers/multi_agent_replay_buffer.py
View file

@ -66,7 +66,6 @@ class MultiAgentReplayBuffer(ReplayBuffer):
capacity: int = 10000,
storage_unit: str = "timesteps",
num_shards: int = 1,
learning_starts: int = 1000,
replay_mode: str = "independent",
replay_sequence_override: bool = True,
replay_sequence_length: int = 1,
@ -84,9 +83,6 @@ class MultiAgentReplayBuffer(ReplayBuffer):
are stored in episodes, replay_sequence_length is ignored.
num_shards: The number of buffer shards that exist in total
(including this one).
learning_starts: Number of timesteps after which a call to
`sample()` will yield samples (before that, `sample()` will
return None).
replay_mode: One of "independent" or "lockstep". Determines,
whether batches are sampled independently or to an equal
amount.
@ -121,7 +117,6 @@ class MultiAgentReplayBuffer(ReplayBuffer):
else:
self.underlying_buffer_call_args = {}
self.replay_sequence_override = replay_sequence_override
self.replay_starts = learning_starts // num_shards
self.replay_mode = replay_mode
self.replay_sequence_length = replay_sequence_length
self.replay_burn_in = replay_burn_in
@ -318,8 +313,6 @@ class MultiAgentReplayBuffer(ReplayBuffer):
# Merge kwargs, overwriting standard call arguments
kwargs = merge_dicts_with_warning(self.underlying_buffer_call_args, kwargs)
if self._num_added < self.replay_starts:
return MultiAgentBatch({}, 0)
with self.replay_timer:
# Lockstep mode: Sample from all policies at the same time an
# equal amount of steps.

12
rllib/utils/replay_buffers/tests/test_multi_agent_mixin_replay_buffer.py
View file

@ -51,7 +51,6 @@ class TestMixInMultiAgentReplayBuffer(unittest.TestCase):
capacity=self.capacity,
storage_unit="episodes",
replay_ratio=0.5,
learning_starts=0,
)
# If we insert and replay n times, expect roughly return batches of
@ -75,7 +74,6 @@ class TestMixInMultiAgentReplayBuffer(unittest.TestCase):
capacity=100,
storage_unit="sequences",
replay_ratio=0.5,
learning_starts=0,
replay_sequence_length=2,
replay_sequence_override=True,
)
@ -99,7 +97,6 @@ class TestMixInMultiAgentReplayBuffer(unittest.TestCase):
capacity=self.capacity,
storage_unit="timesteps",
replay_ratio=0.333,
learning_starts=0,
)
# Expect exactly 0 samples to be returned (buffer empty).
sample = buffer.sample(10)
@ -132,7 +129,8 @@ class TestMixInMultiAgentReplayBuffer(unittest.TestCase):
# 90% replay ratio.
buffer = MultiAgentMixInReplayBuffer(
capacity=self.capacity, replay_ratio=0.9, learning_starts=0
capacity=self.capacity,
replay_ratio=0.9,
)
# If we insert and replay n times, expect roughly return batches of
@ -148,7 +146,8 @@ class TestMixInMultiAgentReplayBuffer(unittest.TestCase):
# 0% replay ratio -> Only new samples.
buffer = MultiAgentMixInReplayBuffer(
capacity=self.capacity, replay_ratio=0.0, learning_starts=0
capacity=self.capacity,
replay_ratio=0.0,
)
# Add a new batch.
batch = self._generate_single_timesteps()
@ -175,7 +174,8 @@ class TestMixInMultiAgentReplayBuffer(unittest.TestCase):
# 100% replay ratio -> Only new samples.
buffer = MultiAgentMixInReplayBuffer(
capacity=self.capacity, replay_ratio=1.0, learning_starts=0
capacity=self.capacity,
replay_ratio=1.0,
)
# Expect exactly 0 samples to be returned (buffer empty).
sample = buffer.sample(1)

8
rllib/utils/replay_buffers/tests/test_multi_agent_prioritized_replay_buffer.py
View file

@ -79,7 +79,9 @@ class TestMultiAgentPrioritizedReplayBuffer(unittest.TestCase):
# Test lockstep mode with different policy ids using MultiAgentBatches
buffer = MultiAgentPrioritizedReplayBuffer(
capacity=10, replay_mode="independent", learning_starts=0, num_shards=1
capacity=10,
replay_mode="independent",
num_shards=1,
)
self._add_multi_agent_batch_to_buffer(buffer, num_policies=1, num_batches=1)
@ -99,7 +101,6 @@ class TestMultiAgentPrioritizedReplayBuffer(unittest.TestCase):
buffer = MultiAgentPrioritizedReplayBuffer(
capacity=buffer_size,
replay_mode="lockstep",
learning_starts=0,
num_shards=1,
)
@ -131,7 +132,6 @@ class TestMultiAgentPrioritizedReplayBuffer(unittest.TestCase):
buffer = MultiAgentPrioritizedReplayBuffer(
capacity=buffer_size,
replay_mode="independent",
learning_starts=0,
num_shards=1,
)
@ -173,7 +173,6 @@ class TestMultiAgentPrioritizedReplayBuffer(unittest.TestCase):
prioritized_replay_beta=self.beta,
replay_mode="independent",
replay_sequence_length=2,
learning_starts=0,
num_shards=1,
)
@ -222,7 +221,6 @@ class TestMultiAgentPrioritizedReplayBuffer(unittest.TestCase):
prioritized_replay_alpha=self.alpha,
prioritized_replay_beta=self.beta,
replay_mode="independent",
learning_starts=0,
num_shards=1,
)
new_buffer.set_state(state)

13
rllib/utils/replay_buffers/tests/test_multi_agent_replay_buffer.py
View file

@ -85,7 +85,9 @@ class TestMultiAgentReplayBuffer(unittest.TestCase):
# Test lockstep mode with different policy ids using MultiAgentBatches
buffer = MultiAgentReplayBuffer(
capacity=10, replay_mode="independent", learning_starts=0, num_shards=1
capacity=10,
replay_mode="independent",
num_shards=1,
)
self._add_multi_agent_batch_to_buffer(buffer, num_policies=1, num_batches=1)
@ -105,7 +107,6 @@ class TestMultiAgentReplayBuffer(unittest.TestCase):
buffer = MultiAgentReplayBuffer(
capacity=buffer_size,
replay_mode="lockstep",
learning_starts=0,
num_shards=1,
)
@ -142,7 +143,6 @@ class TestMultiAgentReplayBuffer(unittest.TestCase):
replay_mode="independent",
storage_unit="sequences",
replay_sequence_length=2,
learning_starts=0,
num_shards=1,
)
@ -188,7 +188,7 @@ class TestMultiAgentReplayBuffer(unittest.TestCase):
buffer = MultiAgentReplayBuffer(
capacity=buffer_size,
replay_mode="independent",
learning_starts=0,
num_steps_sampled_before_learning_starts=0,
num_shards=1,
)
@ -234,7 +234,6 @@ class TestMultiAgentReplayBuffer(unittest.TestCase):
buffer = MultiAgentReplayBuffer(
capacity=buffer_size,
replay_mode="lockstep",
learning_starts=0,
num_shards=1,
underlying_buffer_config=replay_buffer_config,
)
@ -280,7 +279,6 @@ class TestMultiAgentReplayBuffer(unittest.TestCase):
buffer = MultiAgentReplayBuffer(
capacity=buffer_size,
replay_mode="independent",
learning_starts=0,
num_shards=1,
underlying_buffer_config=prioritized_replay_buffer_config,
)
@ -302,7 +300,6 @@ class TestMultiAgentReplayBuffer(unittest.TestCase):
buffer = MultiAgentReplayBuffer(
capacity=buffer_size,
replay_mode="independent",
learning_starts=0,
num_shards=1,
)
@ -315,7 +312,7 @@ class TestMultiAgentReplayBuffer(unittest.TestCase):
another_buffer = MultiAgentReplayBuffer(
capacity=buffer_size,
replay_mode="independent",
learning_starts=0,
num_steps_sampled_before_learning_starts=0,
num_shards=1,
)

14
rllib/utils/replay_buffers/utils.py
View file

@ -238,7 +238,6 @@ def validate_buffer_config(config: dict) -> None:
"prioritized_replay_eps",
"no_local_replay_buffer",
"replay_zero_init_states",
"learning_starts",
"replay_buffer_shards_colocated_with_driver",
]
for k in keys_with_deprecated_positions:
@ -262,6 +261,19 @@ def validate_buffer_config(config: dict) -> None:
)
config["replay_buffer_config"]["replay_mode"] = replay_mode
learning_starts = config.get(
"learning_starts",
config.get("replay_buffer_config", {}).get("learning_starts", DEPRECATED_VALUE),
)
if learning_starts != DEPRECATED_VALUE:
deprecation_warning(
old="config['learning_starts'] or"
"config['replay_buffer_config']['learning_starts']",
help="config['num_steps_sampled_before_learning_starts']",
error=False,
)
config["num_steps_sampled_before_learning_starts"] = learning_starts
# Can't use DEPRECATED_VALUE here because this is also a deliberate
# value set for some algorithms
# TODO: (Artur): Compare to DEPRECATED_VALUE on deprecation