ray/release/rllib_tests/performance_tests/performance_tests.yaml

171 lines
4.7 KiB
YAML

a3c-pongdeterministic-v4:
env: PongDeterministic-v4
run: A3C
# TODO(sven, jungong): fix A3C on torch and re-enable.
frameworks: [ "tf", "tf2" ]
stop:
time_total_s: 3600
config:
num_gpus: 0
num_workers: 16
rollout_fragment_length: 20
vf_loss_coeff: 0.5
entropy_coeff: 0.01
gamma: 0.99
grad_clip: 40.0
lambda: 1.0
lr: 0.0001
observation_filter: NoFilter
preprocessor_pref: rllib
model:
use_lstm: true
conv_activation: elu
dim: 42
grayscale: true
zero_mean: false
# Reduced channel depth and kernel size from default.
conv_filters: [
[32, [3, 3], 2],
[32, [3, 3], 2],
[32, [3, 3], 2],
[32, [3, 3], 2],
]
# TODO(jungong) : flip to True after we have collected some data.
_disable_execution_plan_api: false
apex-breakoutnoframeskip-v4:
env: BreakoutNoFrameskip-v4
run: APEX
frameworks: [ "tf", "tf2", "torch" ]
stop:
time_total_s: 3600
config:
double_q: false
dueling: false
num_atoms: 1
noisy: false
n_step: 3
lr: .0001
adam_epsilon: .00015
hiddens: [512]
buffer_size: 1000000
exploration_config:
epsilon_timesteps: 200000
final_epsilon: 0.01
prioritized_replay_alpha: 0.5
final_prioritized_replay_beta: 1.0
prioritized_replay_beta_annealing_timesteps: 2000000
num_gpus: 1
num_workers: 8
num_envs_per_worker: 8
rollout_fragment_length: 20
train_batch_size: 512
target_network_update_freq: 50000
timesteps_per_iteration: 25000
appo-pongnoframeskip-v4:
env: PongNoFrameskip-v4
run: APPO
frameworks: [ "tf", "tf2", "torch" ]
stop:
time_total_s: 2000
config:
vtrace: True
use_kl_loss: False
rollout_fragment_length: 50
train_batch_size: 750
num_workers: 31
broadcast_interval: 1
max_sample_requests_in_flight_per_worker: 1
num_multi_gpu_tower_stacks: 1
num_envs_per_worker: 8
num_sgd_iter: 2
vf_loss_coeff: 1.0
clip_param: 0.3
num_gpus: 1
grad_clip: 10
model:
dim: 42
cql-halfcheetahbulletenv-v0:
env: HalfCheetahBulletEnv-v0
run: CQL
frameworks: [ "tf", "tf2", "torch" ]
stop:
time_total_s: 3600
config:
# Use input produced by expert SAC algo.
input: ["~/halfcheetah_expert_sac.zip"]
actions_in_input_normalized: true
soft_horizon: False
horizon: 1000
Q_model:
fcnet_activation: relu
fcnet_hiddens: [256, 256, 256]
policy_model:
fcnet_activation: relu
fcnet_hiddens: [256, 256, 256]
tau: 0.005
target_entropy: auto
no_done_at_end: false
n_step: 3
rollout_fragment_length: 1
prioritized_replay: false
train_batch_size: 256
target_network_update_freq: 0
timesteps_per_iteration: 1000
learning_starts: 256
optimization:
actor_learning_rate: 0.0001
critic_learning_rate: 0.0003
entropy_learning_rate: 0.0001
num_workers: 0
num_gpus: 1
metrics_smoothing_episodes: 5
# CQL Configs
min_q_weight: 5.0
bc_iters: 20000
temperature: 1.0
num_actions: 10
lagrangian: False
# Switch on online evaluation.
evaluation_interval: 3
evaluation_config:
input: sampler
always_attach_evaluation_results: True
sac-halfcheetahbulletenv-v0:
env: HalfCheetahBulletEnv-v0
run: SAC
frameworks: [ "tf", "tf2", "torch" ]
stop:
time_total_s: 3600
config:
horizon: 1000
soft_horizon: false
Q_model:
fcnet_activation: relu
fcnet_hiddens: [256, 256]
policy_model:
fcnet_activation: relu
fcnet_hiddens: [256, 256]
tau: 0.005
target_entropy: auto
no_done_at_end: false
n_step: 3
rollout_fragment_length: 1
prioritized_replay: true
train_batch_size: 256
target_network_update_freq: 1
timesteps_per_iteration: 1000
learning_starts: 10000
optimization:
actor_learning_rate: 0.0003
critic_learning_rate: 0.0003
entropy_learning_rate: 0.0003
num_workers: 0
num_gpus: 1
metrics_smoothing_episodes: 5