import numpy as np import unittest import ray from ray.rllib.agents.impala.vtrace_policy import BEHAVIOUR_LOGITS import ray.rllib.agents.ppo as ppo from ray.rllib.agents.ppo.ppo_tf_policy import postprocess_ppo_gae as \ postprocess_ppo_gae_tf, ppo_surrogate_loss as ppo_surrogate_loss_tf from ray.rllib.agents.ppo.ppo_torch_policy import postprocess_ppo_gae as \ postprocess_ppo_gae_torch, ppo_surrogate_loss as ppo_surrogate_loss_torch from ray.rllib.evaluation.postprocessing import Postprocessing from ray.rllib.models.tf.tf_action_dist import Categorical from ray.rllib.models.torch.torch_modelv2 import TorchModelV2 from ray.rllib.models.torch.torch_action_dist import TorchCategorical from ray.rllib.policy.policy import ACTION_LOGP from ray.rllib.policy.sample_batch import SampleBatch from ray.rllib.utils.numpy import fc from ray.rllib.utils.test_utils import check class TestPPO(unittest.TestCase): ray.init() def test_ppo_compilation(self): """Test whether a PPOTrainer can be built with both frameworks.""" config = ppo.DEFAULT_CONFIG.copy() config["num_workers"] = 0 # Run locally. # tf. trainer = ppo.PPOTrainer(config=config, env="CartPole-v0") num_iterations = 2 for i in range(num_iterations): trainer.train() # Torch. config["use_pytorch"] = True config["simple_optimizer"] = True trainer = ppo.PPOTrainer(config=config, env="CartPole-v0") for i in range(num_iterations): trainer.train() def test_ppo_loss_function(self): """Tests the PPO loss function math.""" config = ppo.DEFAULT_CONFIG.copy() config["num_workers"] = 0 # Run locally. config["eager"] = True config["gamma"] = 0.99 config["model"]["fcnet_hiddens"] = [10] config["model"]["fcnet_activation"] = "linear" # Fake CartPole episode of n time steps. train_batch = { SampleBatch.CUR_OBS: np.array( [[0.1, 0.2, 0.3, 0.4], [0.5, 0.6, 0.7, 0.8], [0.9, 1.0, 1.1, 1.2]], dtype=np.float32), SampleBatch.ACTIONS: np.array([0, 1, 1]), SampleBatch.REWARDS: np.array([1.0, -1.0, .5], dtype=np.float32), SampleBatch.DONES: np.array([False, False, True]), SampleBatch.VF_PREDS: np.array([0.5, 0.6, 0.7], dtype=np.float32), BEHAVIOUR_LOGITS: np.array( [[-2., 0.5], [-3., -0.3], [-0.1, 2.5]], dtype=np.float32), ACTION_LOGP: np.array([-0.5, -0.1, -0.2], dtype=np.float32) } # tf. trainer = ppo.PPOTrainer(config=config, env="CartPole-v0") policy = trainer.get_policy() # Post-process (calculate simple (non-GAE) advantages) and attach to # train_batch dict. # A = [0.99^2 * 0.5 + 0.99 * -1.0 + 1.0, 0.99 * 0.5 - 1.0, 0.5] = # [0.50005, -0.505, 0.5] train_batch = postprocess_ppo_gae_tf(policy, train_batch) # Check Advantage values. check(train_batch[Postprocessing.VALUE_TARGETS], [0.50005, -0.505, 0.5]) # Calculate actual PPO loss (results are stored in policy.loss_obj) for # tf. ppo_surrogate_loss_tf(policy, policy.model, Categorical, train_batch) vars = policy.model.trainable_variables() expected_logits = fc( fc(train_batch[SampleBatch.CUR_OBS], vars[0].numpy(), vars[1].numpy()), vars[4].numpy(), vars[5].numpy()) expected_value_outs = fc( fc(train_batch[SampleBatch.CUR_OBS], vars[2].numpy(), vars[3].numpy()), vars[6].numpy(), vars[7].numpy()) kl, entropy, pg_loss, vf_loss, overall_loss = \ self._ppo_loss_helper( policy, policy.model, Categorical, train_batch, expected_logits, expected_value_outs ) check(kl, policy.loss_obj.mean_kl) check(entropy, policy.loss_obj.mean_entropy) check(np.mean(-pg_loss), policy.loss_obj.mean_policy_loss) check(np.mean(vf_loss), policy.loss_obj.mean_vf_loss, decimals=4) check(policy.loss_obj.loss.numpy(), overall_loss, decimals=4) # Torch. config["use_pytorch"] = True trainer = ppo.PPOTrainer(config=config, env="CartPole-v0") policy = trainer.get_policy() train_batch = postprocess_ppo_gae_torch(policy, train_batch) train_batch = policy._lazy_tensor_dict(train_batch) # Check Advantage values. check(train_batch[Postprocessing.VALUE_TARGETS], [0.50005, -0.505, 0.5]) # Calculate actual PPO loss (results are stored in policy.loss_obj) # for tf. ppo_surrogate_loss_torch(policy, policy.model, TorchCategorical, train_batch) kl, entropy, pg_loss, vf_loss, overall_loss = \ self._ppo_loss_helper( policy, policy.model, TorchCategorical, train_batch, policy.model.last_output(), policy.model.value_function().detach().numpy() ) check(kl, policy.loss_obj.mean_kl.detach().numpy()) check(entropy, policy.loss_obj.mean_entropy.detach().numpy()) check( np.mean(-pg_loss), policy.loss_obj.mean_policy_loss.detach().numpy()) check( np.mean(vf_loss), policy.loss_obj.mean_vf_loss.detach().numpy(), decimals=4) check(policy.loss_obj.loss.detach().numpy(), overall_loss, decimals=4) def _ppo_loss_helper(self, policy, model, dist_class, train_batch, logits, vf_outs): """ Calculates the expected PPO loss (components) given Policy, Model, distribution, some batch, logits & vf outputs, using numpy. """ # Calculate expected PPO loss results. dist = dist_class(logits, policy.model) dist_prev = dist_class(train_batch[BEHAVIOUR_LOGITS], policy.model) expected_logp = dist.logp(train_batch[SampleBatch.ACTIONS]) if isinstance(model, TorchModelV2): expected_rho = np.exp(expected_logp.detach().numpy() - train_batch.get(ACTION_LOGP)) # KL(prev vs current action dist)-loss component. kl = np.mean(dist_prev.kl(dist).detach().numpy()) # Entropy-loss component. entropy = np.mean(dist.entropy().detach().numpy()) else: expected_rho = np.exp(expected_logp - train_batch[ACTION_LOGP]) # KL(prev vs current action dist)-loss component. kl = np.mean(dist_prev.kl(dist)) # Entropy-loss component. entropy = np.mean(dist.entropy()) # Policy loss component. pg_loss = np.minimum( train_batch.get(Postprocessing.ADVANTAGES) * expected_rho, train_batch.get(Postprocessing.ADVANTAGES) * np.clip( expected_rho, 1 - policy.config["clip_param"], 1 + policy.config["clip_param"])) # Value function loss component. vf_loss1 = np.power( vf_outs - train_batch.get(Postprocessing.VALUE_TARGETS), 2.0) vf_clipped = train_batch.get(SampleBatch.VF_PREDS) + np.clip( vf_outs - train_batch.get(SampleBatch.VF_PREDS), -policy.config["vf_clip_param"], policy.config["vf_clip_param"]) vf_loss2 = np.power( vf_clipped - train_batch.get(Postprocessing.VALUE_TARGETS), 2.0) vf_loss = np.maximum(vf_loss1, vf_loss2) # Overall loss. overall_loss = np.mean(-pg_loss + policy.kl_coeff * kl + policy.config["vf_loss_coeff"] * vf_loss - policy.entropy_coeff * entropy) return kl, entropy, pg_loss, vf_loss, overall_loss