mirror of
https://github.com/vale981/ray
synced 2025-03-09 12:56:46 -04:00
398 lines
16 KiB
Python
398 lines
16 KiB
Python
import copy
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import numpy as np
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import unittest
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import ray
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from ray.rllib.agents.callbacks import DefaultCallbacks
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import ray.rllib.agents.ppo as ppo
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from ray.rllib.agents.ppo.ppo_tf_policy import ppo_surrogate_loss as \
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ppo_surrogate_loss_tf
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from ray.rllib.agents.ppo.ppo_torch_policy import ppo_surrogate_loss as \
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ppo_surrogate_loss_torch
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from ray.rllib.evaluation.postprocessing import compute_gae_for_sample_batch, \
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Postprocessing
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from ray.rllib.models.tf.tf_action_dist import Categorical
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from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
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from ray.rllib.models.torch.torch_action_dist import TorchCategorical
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from ray.rllib.policy.sample_batch import SampleBatch
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from ray.rllib.utils.numpy import fc
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from ray.rllib.utils.test_utils import check, framework_iterator, \
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check_compute_single_action
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# Fake CartPole episode of n time steps.
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FAKE_BATCH = {
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SampleBatch.OBS: np.array(
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[[0.1, 0.2, 0.3, 0.4], [0.5, 0.6, 0.7, 0.8], [0.9, 1.0, 1.1, 1.2]],
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dtype=np.float32),
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SampleBatch.ACTIONS: np.array([0, 1, 1]),
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SampleBatch.PREV_ACTIONS: np.array([0, 1, 1]),
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SampleBatch.REWARDS: np.array([1.0, -1.0, .5], dtype=np.float32),
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SampleBatch.PREV_REWARDS: np.array([1.0, -1.0, .5], dtype=np.float32),
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SampleBatch.DONES: np.array([False, False, True]),
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SampleBatch.VF_PREDS: np.array([0.5, 0.6, 0.7], dtype=np.float32),
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SampleBatch.ACTION_DIST_INPUTS: np.array(
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[[-2., 0.5], [-3., -0.3], [-0.1, 2.5]], dtype=np.float32),
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SampleBatch.ACTION_LOGP: np.array([-0.5, -0.1, -0.2], dtype=np.float32),
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SampleBatch.EPS_ID: np.array([0, 0, 0]),
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SampleBatch.AGENT_INDEX: np.array([0, 0, 0]),
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}
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class MyCallbacks(DefaultCallbacks):
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@staticmethod
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def _check_lr_torch(policy, policy_id):
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for j, opt in enumerate(policy._optimizers):
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for p in opt.param_groups:
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assert p["lr"] == policy.cur_lr, "LR scheduling error!"
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@staticmethod
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def _check_lr_tf(policy, policy_id):
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lr = policy.cur_lr
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sess = policy.get_session()
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if sess:
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lr = sess.run(lr)
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optim_lr = sess.run(policy._optimizer._lr)
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else:
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lr = lr.numpy()
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optim_lr = policy._optimizer.lr.numpy()
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assert lr == optim_lr, "LR scheduling error!"
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def on_train_result(self, *, trainer, result: dict, **kwargs):
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trainer.workers.foreach_policy(self._check_lr_torch if trainer.config[
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"framework"] == "torch" else self._check_lr_tf)
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class TestPPO(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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ray.init()
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@classmethod
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def tearDownClass(cls):
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ray.shutdown()
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def test_ppo_compilation_and_lr_schedule(self):
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"""Test whether a PPOTrainer can be built with all frameworks."""
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config = copy.deepcopy(ppo.DEFAULT_CONFIG)
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# For checking lr-schedule correctness.
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config["callbacks"] = MyCallbacks
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config["num_workers"] = 1
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config["num_sgd_iter"] = 2
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# Settings in case we use an LSTM.
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config["model"]["lstm_cell_size"] = 10
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config["model"]["max_seq_len"] = 20
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config["train_batch_size"] = 128
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# Test with compression.
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config["compress_observations"] = True
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num_iterations = 2
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for _ in framework_iterator(config):
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for env in ["CartPole-v0", "MsPacmanNoFrameskip-v4"]:
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print("Env={}".format(env))
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for lstm in [False, True]:
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print("LSTM={}".format(lstm))
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config["model"]["use_lstm"] = lstm
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config["model"]["lstm_use_prev_action"] = lstm
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config["model"]["lstm_use_prev_reward"] = lstm
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trainer = ppo.PPOTrainer(config=config, env=env)
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for i in range(num_iterations):
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trainer.train()
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check_compute_single_action(
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trainer,
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include_prev_action_reward=True,
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include_state=lstm)
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trainer.stop()
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def test_ppo_fake_multi_gpu_learning(self):
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"""Test whether PPOTrainer can learn CartPole w/ faked multi-GPU."""
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config = copy.deepcopy(ppo.DEFAULT_CONFIG)
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# Fake GPU setup.
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config["num_gpus"] = 2
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config["_fake_gpus"] = True
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config["framework"] = "tf"
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# Mimic tuned_example for PPO CartPole.
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config["num_workers"] = 1
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config["lr"] = 0.0003
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config["observation_filter"] = "MeanStdFilter"
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config["num_sgd_iter"] = 6
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config["vf_loss_coeff"] = 0.01
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config["model"]["fcnet_hiddens"] = [32]
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config["model"]["fcnet_activation"] = "linear"
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config["model"]["vf_share_layers"] = True
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trainer = ppo.PPOTrainer(config=config, env="CartPole-v0")
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num_iterations = 200
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learnt = False
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for i in range(num_iterations):
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results = trainer.train()
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print(results)
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if results["episode_reward_mean"] > 75.0:
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learnt = True
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break
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assert learnt, "PPO multi-GPU (with fake-GPUs) did not learn CartPole!"
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trainer.stop()
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def test_ppo_exploration_setup(self):
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"""Tests, whether PPO runs with different exploration setups."""
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config = copy.deepcopy(ppo.DEFAULT_CONFIG)
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config["num_workers"] = 0 # Run locally.
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config["env_config"] = {"is_slippery": False, "map_name": "4x4"}
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obs = np.array(0)
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# Test against all frameworks.
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for fw in framework_iterator(config):
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# Default Agent should be setup with StochasticSampling.
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trainer = ppo.PPOTrainer(config=config, env="FrozenLake-v0")
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# explore=False, always expect the same (deterministic) action.
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a_ = trainer.compute_action(
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obs,
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explore=False,
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prev_action=np.array(2),
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prev_reward=np.array(1.0))
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# Test whether this is really the argmax action over the logits.
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if fw != "tf":
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last_out = trainer.get_policy().model.last_output()
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if fw == "torch":
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check(a_, np.argmax(last_out.detach().cpu().numpy(), 1)[0])
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else:
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check(a_, np.argmax(last_out.numpy(), 1)[0])
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for _ in range(50):
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a = trainer.compute_action(
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obs,
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explore=False,
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prev_action=np.array(2),
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prev_reward=np.array(1.0))
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check(a, a_)
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# With explore=True (default), expect stochastic actions.
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actions = []
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for _ in range(300):
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actions.append(
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trainer.compute_action(
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obs,
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prev_action=np.array(2),
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prev_reward=np.array(1.0)))
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check(np.mean(actions), 1.5, atol=0.2)
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trainer.stop()
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def test_ppo_free_log_std(self):
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"""Tests the free log std option works."""
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config = copy.deepcopy(ppo.DEFAULT_CONFIG)
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config["num_workers"] = 0 # Run locally.
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config["gamma"] = 0.99
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config["model"]["fcnet_hiddens"] = [10]
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config["model"]["fcnet_activation"] = "linear"
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config["model"]["free_log_std"] = True
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config["model"]["vf_share_layers"] = True
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for fw, sess in framework_iterator(config, session=True):
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trainer = ppo.PPOTrainer(config=config, env="CartPole-v0")
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policy = trainer.get_policy()
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# Check the free log std var is created.
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if fw == "torch":
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matching = [
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v for (n, v) in policy.model.named_parameters()
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if "log_std" in n
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]
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else:
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matching = [
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v for v in policy.model.trainable_variables()
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if "log_std" in str(v)
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]
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assert len(matching) == 1, matching
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log_std_var = matching[0]
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def get_value():
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if fw == "tf":
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return policy.get_session().run(log_std_var)[0]
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elif fw == "torch":
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return log_std_var.detach().cpu().numpy()[0]
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else:
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return log_std_var.numpy()[0]
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# Check the variable is initially zero.
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init_std = get_value()
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assert init_std == 0.0, init_std
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batch = compute_gae_for_sample_batch(policy, FAKE_BATCH.copy())
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if fw == "torch":
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batch = policy._lazy_tensor_dict(batch)
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policy.learn_on_batch(batch)
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# Check the variable is updated.
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post_std = get_value()
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assert post_std != 0.0, post_std
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trainer.stop()
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def test_ppo_loss_function(self):
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"""Tests the PPO loss function math."""
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config = copy.deepcopy(ppo.DEFAULT_CONFIG)
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config["num_workers"] = 0 # Run locally.
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config["gamma"] = 0.99
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config["model"]["fcnet_hiddens"] = [10]
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config["model"]["fcnet_activation"] = "linear"
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config["model"]["vf_share_layers"] = True
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for fw, sess in framework_iterator(config, session=True):
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trainer = ppo.PPOTrainer(config=config, env="CartPole-v0")
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policy = trainer.get_policy()
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# Check no free log std var by default.
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if fw == "torch":
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matching = [
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v for (n, v) in policy.model.named_parameters()
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if "log_std" in n
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]
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else:
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matching = [
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v for v in policy.model.trainable_variables()
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if "log_std" in str(v)
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]
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assert len(matching) == 0, matching
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# Post-process (calculate simple (non-GAE) advantages) and attach
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# to train_batch dict.
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# A = [0.99^2 * 0.5 + 0.99 * -1.0 + 1.0, 0.99 * 0.5 - 1.0, 0.5] =
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# [0.50005, -0.505, 0.5]
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train_batch = compute_gae_for_sample_batch(policy,
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FAKE_BATCH.copy())
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if fw == "torch":
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train_batch = policy._lazy_tensor_dict(train_batch)
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# Check Advantage values.
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check(train_batch[Postprocessing.VALUE_TARGETS],
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[0.50005, -0.505, 0.5])
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# Calculate actual PPO loss.
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if fw in ["tf2", "tfe"]:
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ppo_surrogate_loss_tf(policy, policy.model, Categorical,
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train_batch)
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elif fw == "torch":
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ppo_surrogate_loss_torch(policy, policy.model,
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TorchCategorical, train_batch)
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vars = policy.model.variables() if fw != "torch" else \
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list(policy.model.parameters())
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if fw == "tf":
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vars = policy.get_session().run(vars)
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expected_shared_out = fc(
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train_batch[SampleBatch.CUR_OBS],
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vars[0 if fw != "torch" else 2],
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vars[1 if fw != "torch" else 3],
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framework=fw)
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expected_logits = fc(
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expected_shared_out,
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vars[2 if fw != "torch" else 0],
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vars[3 if fw != "torch" else 1],
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framework=fw)
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expected_value_outs = fc(
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expected_shared_out, vars[4], vars[5], framework=fw)
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kl, entropy, pg_loss, vf_loss, overall_loss = \
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self._ppo_loss_helper(
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policy, policy.model,
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Categorical if fw != "torch" else TorchCategorical,
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train_batch,
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expected_logits, expected_value_outs,
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sess=sess
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)
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if sess:
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policy_sess = policy.get_session()
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k, e, pl, v, tl = policy_sess.run(
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[
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policy._mean_kl,
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policy._mean_entropy,
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policy._mean_policy_loss,
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policy._mean_vf_loss,
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policy._total_loss,
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],
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feed_dict=policy._get_loss_inputs_dict(
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train_batch, shuffle=False))
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check(k, kl)
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check(e, entropy)
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check(pl, np.mean(-pg_loss))
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check(v, np.mean(vf_loss), decimals=4)
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check(tl, overall_loss, decimals=4)
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else:
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check(policy._mean_kl, kl)
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check(policy._mean_entropy, entropy)
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check(policy._mean_policy_loss, np.mean(-pg_loss))
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check(policy._mean_vf_loss, np.mean(vf_loss), decimals=4)
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check(policy._total_loss, overall_loss, decimals=4)
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trainer.stop()
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def _ppo_loss_helper(self,
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policy,
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model,
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dist_class,
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train_batch,
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logits,
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vf_outs,
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sess=None):
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"""
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Calculates the expected PPO loss (components) given Policy,
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Model, distribution, some batch, logits & vf outputs, using numpy.
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"""
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# Calculate expected PPO loss results.
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dist = dist_class(logits, policy.model)
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dist_prev = dist_class(train_batch[SampleBatch.ACTION_DIST_INPUTS],
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policy.model)
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expected_logp = dist.logp(train_batch[SampleBatch.ACTIONS])
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if isinstance(model, TorchModelV2):
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expected_rho = np.exp(expected_logp.detach().cpu().numpy() -
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train_batch.get(SampleBatch.ACTION_LOGP))
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# KL(prev vs current action dist)-loss component.
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kl = np.mean(dist_prev.kl(dist).detach().cpu().numpy())
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# Entropy-loss component.
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entropy = np.mean(dist.entropy().detach().cpu().numpy())
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else:
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if sess:
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expected_logp = sess.run(expected_logp)
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expected_rho = np.exp(expected_logp -
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train_batch[SampleBatch.ACTION_LOGP])
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# KL(prev vs current action dist)-loss component.
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kl = dist_prev.kl(dist)
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if sess:
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kl = sess.run(kl)
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kl = np.mean(kl)
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# Entropy-loss component.
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entropy = dist.entropy()
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if sess:
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entropy = sess.run(entropy)
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entropy = np.mean(entropy)
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# Policy loss component.
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pg_loss = np.minimum(
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train_batch.get(Postprocessing.ADVANTAGES) * expected_rho,
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train_batch.get(Postprocessing.ADVANTAGES) * np.clip(
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expected_rho, 1 - policy.config["clip_param"],
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1 + policy.config["clip_param"]))
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# Value function loss component.
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vf_loss1 = np.power(
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vf_outs - train_batch.get(Postprocessing.VALUE_TARGETS), 2.0)
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vf_clipped = train_batch.get(SampleBatch.VF_PREDS) + np.clip(
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vf_outs - train_batch.get(SampleBatch.VF_PREDS),
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-policy.config["vf_clip_param"], policy.config["vf_clip_param"])
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vf_loss2 = np.power(
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vf_clipped - train_batch.get(Postprocessing.VALUE_TARGETS), 2.0)
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vf_loss = np.maximum(vf_loss1, vf_loss2)
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# Overall loss.
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if sess:
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policy_sess = policy.get_session()
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kl_coeff, entropy_coeff = policy_sess.run(
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[policy.kl_coeff, policy.entropy_coeff])
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else:
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kl_coeff, entropy_coeff = policy.kl_coeff, policy.entropy_coeff
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overall_loss = np.mean(-pg_loss + kl_coeff * kl +
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policy.config["vf_loss_coeff"] * vf_loss -
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entropy_coeff * entropy)
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return kl, entropy, pg_loss, vf_loss, overall_loss
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if __name__ == "__main__":
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import pytest
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import sys
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sys.exit(pytest.main(["-v", __file__]))
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