import numpy as np import re import unittest import ray import ray.rllib.agents.ddpg as ddpg from ray.rllib.agents.ddpg.ddpg_torch_policy import ddpg_actor_critic_loss as \ loss_torch from ray.rllib.agents.sac.tests.test_sac import SimpleEnv from ray.rllib.execution.replay_buffer import LocalReplayBuffer from ray.rllib.policy.sample_batch import SampleBatch from ray.rllib.utils.framework import try_import_tf, try_import_torch from ray.rllib.utils.numpy import fc, huber_loss, l2_loss, relu, sigmoid from ray.rllib.utils.test_utils import check, check_compute_single_action, \ framework_iterator from ray.rllib.utils.torch_ops import convert_to_torch_tensor tf1, tf, tfv = try_import_tf() torch, _ = try_import_torch() class TestDDPG(unittest.TestCase): @classmethod def setUpClass(cls) -> None: ray.init() @classmethod def tearDownClass(cls) -> None: ray.shutdown() def test_ddpg_compilation(self): """Test whether a DDPGTrainer can be built with both frameworks.""" config = ddpg.DEFAULT_CONFIG.copy() config["num_workers"] = 1 config["num_envs_per_worker"] = 2 config["learning_starts"] = 0 config["exploration_config"]["random_timesteps"] = 100 num_iterations = 1 # Test against all frameworks. for _ in framework_iterator(config): trainer = ddpg.DDPGTrainer(config=config, env="Pendulum-v0") for i in range(num_iterations): results = trainer.train() print(results) check_compute_single_action(trainer) trainer.stop() def test_ddpg_exploration_and_with_random_prerun(self): """Tests DDPG's Exploration (w/ random actions for n timesteps).""" core_config = ddpg.DEFAULT_CONFIG.copy() core_config["num_workers"] = 0 # Run locally. obs = np.array([0.0, 0.1, -0.1]) # Test against all frameworks. for _ in framework_iterator(core_config): config = core_config.copy() # Default OUNoise setup. trainer = ddpg.DDPGTrainer(config=config, env="Pendulum-v0") # Setting explore=False should always return the same action. a_ = trainer.compute_action(obs, explore=False) for _ in range(50): a = trainer.compute_action(obs, explore=False) check(a, a_) # explore=None (default: explore) should return different actions. actions = [] for _ in range(50): actions.append(trainer.compute_action(obs)) check(np.std(actions), 0.0, false=True) trainer.stop() # Check randomness at beginning. config["exploration_config"] = { # Act randomly at beginning ... "random_timesteps": 50, # Then act very closely to deterministic actions thereafter. "ou_base_scale": 0.001, "initial_scale": 0.001, "final_scale": 0.001, } trainer = ddpg.DDPGTrainer(config=config, env="Pendulum-v0") # ts=1 (get a deterministic action as per explore=False). deterministic_action = trainer.compute_action(obs, explore=False) # ts=2-5 (in random window). random_a = [] for _ in range(49): random_a.append(trainer.compute_action(obs, explore=True)) check(random_a[-1], deterministic_action, false=True) self.assertTrue(np.std(random_a) > 0.5) # ts > 50 (a=deterministic_action + scale * N[0,1]) for _ in range(50): a = trainer.compute_action(obs, explore=True) check(a, deterministic_action, rtol=0.1) # ts >> 50 (BUT: explore=False -> expect deterministic action). for _ in range(50): a = trainer.compute_action(obs, explore=False) check(a, deterministic_action) trainer.stop() def test_ddpg_loss_function(self): """Tests DDPG loss function results across all frameworks.""" config = ddpg.DEFAULT_CONFIG.copy() # Run locally. config["num_workers"] = 0 config["learning_starts"] = 0 config["twin_q"] = True config["use_huber"] = True config["huber_threshold"] = 1.0 config["gamma"] = 0.99 # Make this small (seems to introduce errors). config["l2_reg"] = 1e-10 config["prioritized_replay"] = False # Use very simple nets. config["actor_hiddens"] = [10] config["critic_hiddens"] = [10] # Make sure, timing differences do not affect trainer.train(). config["min_iter_time_s"] = 0 config["timesteps_per_iteration"] = 100 map_ = { # Normal net. "default_policy/actor_hidden_0/kernel": "policy_model.action_0." "_model.0.weight", "default_policy/actor_hidden_0/bias": "policy_model.action_0." "_model.0.bias", "default_policy/actor_out/kernel": "policy_model.action_out." "_model.0.weight", "default_policy/actor_out/bias": "policy_model.action_out." "_model.0.bias", "default_policy/sequential/q_hidden_0/kernel": "q_model.q_hidden_0" "._model.0.weight", "default_policy/sequential/q_hidden_0/bias": "q_model.q_hidden_0." "_model.0.bias", "default_policy/sequential/q_out/kernel": "q_model.q_out._model." "0.weight", "default_policy/sequential/q_out/bias": "q_model.q_out._model." "0.bias", # -- twin. "default_policy/sequential_1/twin_q_hidden_0/kernel": "twin_" "q_model.twin_q_hidden_0._model.0.weight", "default_policy/sequential_1/twin_q_hidden_0/bias": "twin_" "q_model.twin_q_hidden_0._model.0.bias", "default_policy/sequential_1/twin_q_out/kernel": "twin_" "q_model.twin_q_out._model.0.weight", "default_policy/sequential_1/twin_q_out/bias": "twin_" "q_model.twin_q_out._model.0.bias", # Target net. "default_policy/actor_hidden_0_1/kernel": "policy_model.action_0." "_model.0.weight", "default_policy/actor_hidden_0_1/bias": "policy_model.action_0." "_model.0.bias", "default_policy/actor_out_1/kernel": "policy_model.action_out." "_model.0.weight", "default_policy/actor_out_1/bias": "policy_model.action_out._model" ".0.bias", "default_policy/sequential_2/q_hidden_0/kernel": "q_model." "q_hidden_0._model.0.weight", "default_policy/sequential_2/q_hidden_0/bias": "q_model." "q_hidden_0._model.0.bias", "default_policy/sequential_2/q_out/kernel": "q_model." "q_out._model.0.weight", "default_policy/sequential_2/q_out/bias": "q_model." "q_out._model.0.bias", # -- twin. "default_policy/sequential_3/twin_q_hidden_0/kernel": "twin_" "q_model.twin_q_hidden_0._model.0.weight", "default_policy/sequential_3/twin_q_hidden_0/bias": "twin_" "q_model.twin_q_hidden_0._model.0.bias", "default_policy/sequential_3/twin_q_out/kernel": "twin_" "q_model.twin_q_out._model.0.weight", "default_policy/sequential_3/twin_q_out/bias": "twin_" "q_model.twin_q_out._model.0.bias", } env = SimpleEnv batch_size = 100 if env is SimpleEnv: obs_size = (batch_size, 1) actions = np.random.random(size=(batch_size, 1)) elif env == "CartPole-v0": obs_size = (batch_size, 4) actions = np.random.randint(0, 2, size=(batch_size, )) else: obs_size = (batch_size, 3) actions = np.random.random(size=(batch_size, 1)) # Batch of size=n. input_ = self._get_batch_helper(obs_size, actions, batch_size) # Simply compare loss values AND grads of all frameworks with each # other. prev_fw_loss = weights_dict = None expect_c, expect_a, expect_t = None, None, None # History of tf-updated NN-weights over n training steps. tf_updated_weights = [] # History of input batches used. tf_inputs = [] for fw, sess in framework_iterator( config, frameworks=("tf", "torch"), session=True): # Generate Trainer and get its default Policy object. trainer = ddpg.DDPGTrainer(config=config, env=env) policy = trainer.get_policy() p_sess = None if sess: p_sess = policy.get_session() # Set all weights (of all nets) to fixed values. if weights_dict is None: assert fw == "tf" # Start with the tf vars-dict. weights_dict = policy.get_weights() else: assert fw == "torch" # Then transfer that to torch Model. model_dict = self._translate_weights_to_torch( weights_dict, map_) policy.model.load_state_dict(model_dict) policy.target_model.load_state_dict(model_dict) if fw == "torch": # Actually convert to torch tensors. input_ = policy._lazy_tensor_dict(input_) input_ = {k: input_[k] for k in input_.keys()} # Only run the expectation once, should be the same anyways # for all frameworks. if expect_c is None: expect_c, expect_a, expect_t = \ self._ddpg_loss_helper( input_, weights_dict, sorted(weights_dict.keys()), fw, gamma=config["gamma"], huber_threshold=config["huber_threshold"], l2_reg=config["l2_reg"], sess=sess) # Get actual outs and compare to expectation AND previous # framework. c=critic, a=actor, e=entropy, t=td-error. if fw == "tf": c, a, t, tf_c_grads, tf_a_grads = \ p_sess.run([ policy.critic_loss, policy.actor_loss, policy.td_error, policy._critic_optimizer.compute_gradients( policy.critic_loss, policy.model.q_variables()), policy._actor_optimizer.compute_gradients( policy.actor_loss, policy.model.policy_variables())], feed_dict=policy._get_loss_inputs_dict( input_, shuffle=False)) # Check pure loss values. check(c, expect_c) check(a, expect_a) check(t, expect_t) tf_c_grads = [g for g, v in tf_c_grads] tf_a_grads = [g for g, v in tf_a_grads] elif fw == "torch": loss_torch(policy, policy.model, None, input_) c, a, t = policy.critic_loss, policy.actor_loss, \ policy.td_error # Check pure loss values. check(c, expect_c) check(a, expect_a) check(t, expect_t) # Test actor gradients. policy._actor_optimizer.zero_grad() assert all(v.grad is None for v in policy.model.q_variables()) assert all( v.grad is None for v in policy.model.policy_variables()) a.backward() # `actor_loss` depends on Q-net vars # (but not twin-Q-net vars!). assert not any(v.grad is None for v in policy.model.q_variables()[:4]) assert all( v.grad is None for v in policy.model.q_variables()[4:]) assert not all( torch.mean(v.grad) == 0 for v in policy.model.policy_variables()) assert not all( torch.min(v.grad) == 0 for v in policy.model.policy_variables()) # Compare with tf ones. torch_a_grads = [ v.grad for v in policy.model.policy_variables() ] for tf_g, torch_g in zip(tf_a_grads, torch_a_grads): if tf_g.shape != torch_g.shape: check(tf_g, np.transpose(torch_g)) else: check(tf_g, torch_g) # Test critic gradients. policy._critic_optimizer.zero_grad() assert all( v.grad is None or torch.mean(v.grad) == 0.0 for v in policy.model.q_variables()) assert all( v.grad is None or torch.min(v.grad) == 0.0 for v in policy.model.q_variables()) c.backward() assert not all( torch.mean(v.grad) == 0 for v in policy.model.q_variables()) assert not all( torch.min(v.grad) == 0 for v in policy.model.q_variables()) # Compare with tf ones. torch_c_grads = [v.grad for v in policy.model.q_variables()] for tf_g, torch_g in zip(tf_c_grads, torch_c_grads): if tf_g.shape != torch_g.shape: check(tf_g, np.transpose(torch_g)) else: check(tf_g, torch_g) # Compare (unchanged(!) actor grads) with tf ones. torch_a_grads = [ v.grad for v in policy.model.policy_variables() ] for tf_g, torch_g in zip(tf_a_grads, torch_a_grads): if tf_g.shape != torch_g.shape: check(tf_g, np.transpose(torch_g)) else: check(tf_g, torch_g) # Store this framework's losses in prev_fw_loss to compare with # next framework's outputs. if prev_fw_loss is not None: check(c, prev_fw_loss[0]) check(a, prev_fw_loss[1]) check(t, prev_fw_loss[2]) prev_fw_loss = (c, a, t) # Update weights from our batch (n times). for update_iteration in range(10): print("train iteration {}".format(update_iteration)) if fw == "tf": in_ = self._get_batch_helper(obs_size, actions, batch_size) tf_inputs.append(in_) # Set a fake-batch to use # (instead of sampling from replay buffer). buf = LocalReplayBuffer.get_instance_for_testing() buf._fake_batch = in_ trainer.train() updated_weights = policy.get_weights() # Net must have changed. if tf_updated_weights: check( updated_weights[ "default_policy/actor_hidden_0/kernel"], tf_updated_weights[-1][ "default_policy/actor_hidden_0/kernel"], false=True) tf_updated_weights.append(updated_weights) # Compare with updated tf-weights. Must all be the same. else: tf_weights = tf_updated_weights[update_iteration] in_ = tf_inputs[update_iteration] # Set a fake-batch to use # (instead of sampling from replay buffer). buf = LocalReplayBuffer.get_instance_for_testing() buf._fake_batch = in_ trainer.train() # Compare updated model and target weights. for tf_key in tf_weights.keys(): tf_var = tf_weights[tf_key] # Model. if re.search( "actor_out_1|actor_hidden_0_1|sequential_" "[23]", tf_key): torch_var = policy.target_model.state_dict()[map_[ tf_key]] # Target model. else: torch_var = policy.model.state_dict()[map_[tf_key]] if tf_var.shape != torch_var.shape: check(tf_var, np.transpose(torch_var), atol=0.1) else: check(tf_var, torch_var, atol=0.1) trainer.stop() def _get_batch_helper(self, obs_size, actions, batch_size): return { SampleBatch.CUR_OBS: np.random.random(size=obs_size), SampleBatch.ACTIONS: actions, SampleBatch.REWARDS: np.random.random(size=(batch_size, )), SampleBatch.DONES: np.random.choice( [True, False], size=(batch_size, )), SampleBatch.NEXT_OBS: np.random.random(size=obs_size), "weights": np.ones(shape=(batch_size, )), } def _ddpg_loss_helper(self, train_batch, weights, ks, fw, gamma, huber_threshold, l2_reg, sess): """Emulates DDPG loss functions for tf and torch.""" model_out_t = train_batch[SampleBatch.CUR_OBS] target_model_out_tp1 = train_batch[SampleBatch.NEXT_OBS] # get_policy_output policy_t = sigmoid(2.0 * fc( relu( fc(model_out_t, weights[ks[1]], weights[ks[0]], framework=fw)), weights[ks[5]], weights[ks[4]], framework=fw)) # Get policy output for t+1 (target model). policy_tp1 = sigmoid(2.0 * fc( relu( fc(target_model_out_tp1, weights[ks[3]], weights[ks[2]], framework=fw)), weights[ks[7]], weights[ks[6]], framework=fw)) # Assume no smooth target policy. policy_tp1_smoothed = policy_tp1 # Q-values for the actually selected actions. # get_q_values q_t = fc( relu( fc(np.concatenate( [model_out_t, train_batch[SampleBatch.ACTIONS]], -1), weights[ks[9]], weights[ks[8]], framework=fw)), weights[ks[11]], weights[ks[10]], framework=fw) twin_q_t = fc( relu( fc(np.concatenate( [model_out_t, train_batch[SampleBatch.ACTIONS]], -1), weights[ks[13]], weights[ks[12]], framework=fw)), weights[ks[15]], weights[ks[14]], framework=fw) # Q-values for current policy in given current state. # get_q_values q_t_det_policy = fc( relu( fc(np.concatenate([model_out_t, policy_t], -1), weights[ks[9]], weights[ks[8]], framework=fw)), weights[ks[11]], weights[ks[10]], framework=fw) # Target q network evaluation. # target_model.get_q_values q_tp1 = fc( relu( fc(np.concatenate([target_model_out_tp1, policy_tp1_smoothed], -1), weights[ks[17]], weights[ks[16]], framework=fw)), weights[ks[19]], weights[ks[18]], framework=fw) twin_q_tp1 = fc( relu( fc(np.concatenate([target_model_out_tp1, policy_tp1_smoothed], -1), weights[ks[21]], weights[ks[20]], framework=fw)), weights[ks[23]], weights[ks[22]], framework=fw) q_t_selected = np.squeeze(q_t, axis=-1) twin_q_t_selected = np.squeeze(twin_q_t, axis=-1) q_tp1 = np.minimum(q_tp1, twin_q_tp1) q_tp1_best = np.squeeze(q_tp1, axis=-1) dones = train_batch[SampleBatch.DONES] rewards = train_batch[SampleBatch.REWARDS] if fw == "torch": dones = dones.float().numpy() rewards = rewards.numpy() q_tp1_best_masked = (1.0 - dones) * q_tp1_best q_t_selected_target = rewards + gamma * q_tp1_best_masked td_error = q_t_selected - q_t_selected_target twin_td_error = twin_q_t_selected - q_t_selected_target td_error = td_error + twin_td_error errors = huber_loss(td_error, huber_threshold) + \ huber_loss(twin_td_error, huber_threshold) critic_loss = np.mean(errors) actor_loss = -np.mean(q_t_det_policy) # Add l2-regularization if required. for name, var in weights.items(): if re.match("default_policy/actor_(hidden_0|out)/kernel", name): actor_loss += (l2_reg * l2_loss(var)) elif re.match("default_policy/sequential(_1)?/\\w+/kernel", name): critic_loss += (l2_reg * l2_loss(var)) return critic_loss, actor_loss, td_error def _translate_weights_to_torch(self, weights_dict, map_): model_dict = { map_[k]: convert_to_torch_tensor( np.transpose(v) if re.search("kernel", k) else v) for k, v in weights_dict.items() if re.search( "default_policy/(actor_(hidden_0|out)|sequential(_1)?)/", k) } return model_dict if __name__ == "__main__": import pytest import sys sys.exit(pytest.main(["-v", __file__]))