import numpy as np import unittest import ray import ray.rllib.agents.ddpg as ddpg import ray.rllib.agents.dqn as dqn from ray.rllib.utils.test_utils import check, framework_iterator class TestParameterNoise(unittest.TestCase): @classmethod def setUpClass(cls) -> None: ray.init() @classmethod def tearDownClass(cls) -> None: ray.shutdown() def test_ddpg_parameter_noise(self): self.do_test_parameter_noise_exploration( ddpg.DDPGTrainer, ddpg.DEFAULT_CONFIG, "Pendulum-v0", {}, np.array([1.0, 0.0, -1.0])) def test_dqn_parameter_noise(self): self.do_test_parameter_noise_exploration( dqn.DQNTrainer, dqn.DEFAULT_CONFIG, "FrozenLake-v0", { "is_slippery": False, "map_name": "4x4" }, np.array(0)) def do_test_parameter_noise_exploration(self, trainer_cls, config, env, env_config, obs): """Tests, whether an Agent works with ParameterNoise.""" core_config = config.copy() core_config["num_workers"] = 0 # Run locally. core_config["env_config"] = env_config for fw in framework_iterator(core_config): config = core_config.copy() # Algo with ParameterNoise exploration (config["explore"]=True). # ---- config["exploration_config"] = {"type": "ParameterNoise"} config["explore"] = True trainer = trainer_cls(config=config, env=env) policy = trainer.get_policy() pol_sess = getattr(policy, "_sess", None) # Remove noise that has been added during policy initialization # (exploration.postprocess_trajectory does add noise to measure # the delta). policy.exploration._remove_noise(tf_sess=pol_sess) self.assertFalse(policy.exploration.weights_are_currently_noisy) noise_before = self._get_current_noise(policy, fw) check(noise_before, 0.0) initial_weights = self._get_current_weight(policy, fw) # Pseudo-start an episode and compare the weights before and after. policy.exploration.on_episode_start(policy, tf_sess=pol_sess) self.assertFalse(policy.exploration.weights_are_currently_noisy) noise_after_ep_start = self._get_current_noise(policy, fw) weights_after_ep_start = self._get_current_weight(policy, fw) # Should be the same, as we don't do anything at the beginning of # the episode, only one step later. check(noise_after_ep_start, noise_before) check(initial_weights, weights_after_ep_start) # Setting explore=False should always return the same action. a_ = trainer.compute_action(obs, explore=False) self.assertFalse(policy.exploration.weights_are_currently_noisy) noise = self._get_current_noise(policy, fw) # We sampled the first noise (not zero anymore). check(noise, 0.0, false=True) # But still not applied b/c explore=False. check(self._get_current_weight(policy, fw), initial_weights) for _ in range(10): a = trainer.compute_action(obs, explore=False) check(a, a_) # Noise never gets applied. check(self._get_current_weight(policy, fw), initial_weights) self.assertFalse( policy.exploration.weights_are_currently_noisy) # Explore=None (default: True) should return different actions. # However, this is only due to the underlying epsilon-greedy # exploration. actions = [] current_weight = None for _ in range(10): actions.append(trainer.compute_action(obs)) self.assertTrue(policy.exploration.weights_are_currently_noisy) # Now, noise actually got applied (explore=True). current_weight = self._get_current_weight(policy, fw) check(current_weight, initial_weights, false=True) check(current_weight, initial_weights + noise) check(np.std(actions), 0.0, false=True) # Pseudo-end the episode and compare weights again. # Make sure they are the original ones. policy.exploration.on_episode_end(policy, tf_sess=pol_sess) weights_after_ep_end = self._get_current_weight(policy, fw) check(current_weight - noise, weights_after_ep_end, decimals=5) # DQN with ParameterNoise exploration (config["explore"]=False). # ---- config = core_config.copy() config["exploration_config"] = {"type": "ParameterNoise"} config["explore"] = False trainer = trainer_cls(config=config, env=env) policy = trainer.get_policy() pol_sess = getattr(policy, "_sess", None) # Remove noise that has been added during policy initialization # (exploration.postprocess_trajectory does add noise to measure # the delta). policy.exploration._remove_noise(tf_sess=pol_sess) self.assertFalse(policy.exploration.weights_are_currently_noisy) initial_weights = self._get_current_weight(policy, fw) # Noise before anything (should be 0.0, no episode started yet). noise = self._get_current_noise(policy, fw) check(noise, 0.0) # Pseudo-start an episode and compare the weights before and after # (they should be the same). policy.exploration.on_episode_start(policy, tf_sess=pol_sess) self.assertFalse(policy.exploration.weights_are_currently_noisy) # Should be the same, as we don't do anything at the beginning of # the episode, only one step later. noise = self._get_current_noise(policy, fw) check(noise, 0.0) noisy_weights = self._get_current_weight(policy, fw) check(initial_weights, noisy_weights) # Setting explore=False or None should always return the same # action. a_ = trainer.compute_action(obs, explore=False) # Now we have re-sampled. noise = self._get_current_noise(policy, fw) check(noise, 0.0, false=True) for _ in range(5): a = trainer.compute_action(obs, explore=None) check(a, a_) a = trainer.compute_action(obs, explore=False) check(a, a_) # Pseudo-end the episode and compare weights again. # Make sure they are the original ones (no noise permanently # applied throughout the episode). policy.exploration.on_episode_end(policy, tf_sess=pol_sess) weights_after_episode_end = self._get_current_weight(policy, fw) check(initial_weights, weights_after_episode_end) # Noise should still be the same (re-sampling only happens at # beginning of episode). noise_after = self._get_current_noise(policy, fw) check(noise, noise_after) # Switch off underlying exploration entirely. # ---- config = core_config.copy() if trainer_cls is dqn.DQNTrainer: sub_config = { "type": "EpsilonGreedy", "initial_epsilon": 0.0, # <- no randomness whatsoever "final_epsilon": 0.0, } else: sub_config = { "type": "OrnsteinUhlenbeckNoise", "initial_scale": 0.0, # <- no randomness whatsoever "final_scale": 0.0, "random_timesteps": 0, } config["exploration_config"] = { "type": "ParameterNoise", "sub_exploration": sub_config, } config["explore"] = True trainer = trainer_cls(config=config, env=env) # Now, when we act - even with explore=True - we would expect # the same action for the same input (parameter noise is # deterministic). policy = trainer.get_policy() policy.exploration.on_episode_start(policy, tf_sess=pol_sess) a_ = trainer.compute_action(obs) for _ in range(10): a = trainer.compute_action(obs, explore=True) check(a, a_) def _get_current_noise(self, policy, fw): # If noise not even created yet, return 0.0. if policy.exploration.noise is None: return 0.0 noise = policy.exploration.noise[0][0][0] if fw == "tf": noise = policy.get_session().run(noise) elif fw == "torch": noise = noise.detach().cpu().numpy() else: noise = noise.numpy() return noise def _get_current_weight(self, policy, fw): weights = policy.get_weights() if fw == "torch": # DQN model. if "_hidden_layers.0._model.0.weight" in weights: return weights["_hidden_layers.0._model.0.weight"][0][0] # DDPG model. else: return weights["policy_model.action_0._model.0.weight"][0][0] key = 0 if fw in ["tf2", "tfe"] else list(weights.keys())[0] return weights[key][0][0] if __name__ == "__main__": import pytest import sys sys.exit(pytest.main(["-v", __file__]))