mirror of
https://github.com/vale981/ray
synced 2025-03-06 10:31:39 -05:00
232 lines
9.6 KiB
Python
232 lines
9.6 KiB
Python
import numpy as np
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import unittest
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import ray
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import ray.rllib.agents.ddpg as ddpg
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import ray.rllib.agents.dqn as dqn
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from ray.rllib.utils.test_utils import check, framework_iterator
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class TestParameterNoise(unittest.TestCase):
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@classmethod
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def setUpClass(cls) -> None:
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ray.init(num_cpus=4)
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@classmethod
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def tearDownClass(cls) -> None:
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ray.shutdown()
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def test_ddpg_parameter_noise(self):
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self.do_test_parameter_noise_exploration(
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ddpg.DDPGTrainer,
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ddpg.DEFAULT_CONFIG,
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"Pendulum-v1",
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{},
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np.array([1.0, 0.0, -1.0]),
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)
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def test_dqn_parameter_noise(self):
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self.do_test_parameter_noise_exploration(
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dqn.DQNTrainer,
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dqn.DEFAULT_CONFIG,
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"FrozenLake-v1",
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{"is_slippery": False, "map_name": "4x4"},
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np.array(0),
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)
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def do_test_parameter_noise_exploration(
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self, trainer_cls, config, env, env_config, obs
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):
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"""Tests, whether an Agent works with ParameterNoise."""
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core_config = config.copy()
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core_config["num_workers"] = 0 # Run locally.
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core_config["env_config"] = env_config
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for fw in framework_iterator(core_config):
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config = core_config.copy()
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# Algo with ParameterNoise exploration (config["explore"]=True).
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# ----
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config["exploration_config"] = {"type": "ParameterNoise"}
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config["explore"] = True
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trainer = trainer_cls(config=config, env=env)
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policy = trainer.get_policy()
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pol_sess = policy.get_session()
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# Remove noise that has been added during policy initialization
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# (exploration.postprocess_trajectory does add noise to measure
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# the delta).
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policy.exploration._remove_noise(tf_sess=pol_sess)
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self.assertFalse(policy.exploration.weights_are_currently_noisy)
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noise_before = self._get_current_noise(policy, fw)
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check(noise_before, 0.0)
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initial_weights = self._get_current_weight(policy, fw)
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# Pseudo-start an episode and compare the weights before and after.
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policy.exploration.on_episode_start(policy, tf_sess=pol_sess)
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self.assertFalse(policy.exploration.weights_are_currently_noisy)
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noise_after_ep_start = self._get_current_noise(policy, fw)
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weights_after_ep_start = self._get_current_weight(policy, fw)
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# Should be the same, as we don't do anything at the beginning of
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# the episode, only one step later.
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check(noise_after_ep_start, noise_before)
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check(initial_weights, weights_after_ep_start)
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# Setting explore=False should always return the same action.
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a_ = trainer.compute_single_action(obs, explore=False)
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self.assertFalse(policy.exploration.weights_are_currently_noisy)
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noise = self._get_current_noise(policy, fw)
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# We sampled the first noise (not zero anymore).
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check(noise, 0.0, false=True)
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# But still not applied b/c explore=False.
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check(self._get_current_weight(policy, fw), initial_weights)
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for _ in range(10):
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a = trainer.compute_single_action(obs, explore=False)
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check(a, a_)
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# Noise never gets applied.
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check(self._get_current_weight(policy, fw), initial_weights)
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self.assertFalse(policy.exploration.weights_are_currently_noisy)
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# Explore=None (default: True) should return different actions.
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# However, this is only due to the underlying epsilon-greedy
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# exploration.
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actions = []
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current_weight = None
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for _ in range(10):
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actions.append(trainer.compute_single_action(obs))
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self.assertTrue(policy.exploration.weights_are_currently_noisy)
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# Now, noise actually got applied (explore=True).
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current_weight = self._get_current_weight(policy, fw)
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check(current_weight, initial_weights, false=True)
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check(current_weight, initial_weights + noise)
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check(np.std(actions), 0.0, false=True)
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# Pseudo-end the episode and compare weights again.
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# Make sure they are the original ones.
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policy.exploration.on_episode_end(policy, tf_sess=pol_sess)
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weights_after_ep_end = self._get_current_weight(policy, fw)
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check(current_weight - noise, weights_after_ep_end, decimals=5)
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trainer.stop()
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# DQN with ParameterNoise exploration (config["explore"]=False).
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# ----
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config = core_config.copy()
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config["exploration_config"] = {"type": "ParameterNoise"}
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config["explore"] = False
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trainer = trainer_cls(config=config, env=env)
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policy = trainer.get_policy()
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pol_sess = policy.get_session()
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# Remove noise that has been added during policy initialization
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# (exploration.postprocess_trajectory does add noise to measure
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# the delta).
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policy.exploration._remove_noise(tf_sess=pol_sess)
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self.assertFalse(policy.exploration.weights_are_currently_noisy)
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initial_weights = self._get_current_weight(policy, fw)
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# Noise before anything (should be 0.0, no episode started yet).
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noise = self._get_current_noise(policy, fw)
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check(noise, 0.0)
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# Pseudo-start an episode and compare the weights before and after
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# (they should be the same).
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policy.exploration.on_episode_start(policy, tf_sess=pol_sess)
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self.assertFalse(policy.exploration.weights_are_currently_noisy)
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# Should be the same, as we don't do anything at the beginning of
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# the episode, only one step later.
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noise = self._get_current_noise(policy, fw)
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check(noise, 0.0)
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noisy_weights = self._get_current_weight(policy, fw)
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check(initial_weights, noisy_weights)
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# Setting explore=False or None should always return the same
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# action.
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a_ = trainer.compute_single_action(obs, explore=False)
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# Now we have re-sampled.
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noise = self._get_current_noise(policy, fw)
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check(noise, 0.0, false=True)
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for _ in range(5):
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a = trainer.compute_single_action(obs, explore=None)
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check(a, a_)
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a = trainer.compute_single_action(obs, explore=False)
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check(a, a_)
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# Pseudo-end the episode and compare weights again.
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# Make sure they are the original ones (no noise permanently
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# applied throughout the episode).
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policy.exploration.on_episode_end(policy, tf_sess=pol_sess)
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weights_after_episode_end = self._get_current_weight(policy, fw)
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check(initial_weights, weights_after_episode_end)
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# Noise should still be the same (re-sampling only happens at
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# beginning of episode).
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noise_after = self._get_current_noise(policy, fw)
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check(noise, noise_after)
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trainer.stop()
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# Switch off underlying exploration entirely.
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# ----
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config = core_config.copy()
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if trainer_cls is dqn.DQNTrainer:
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sub_config = {
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"type": "EpsilonGreedy",
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"initial_epsilon": 0.0, # <- no randomness whatsoever
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"final_epsilon": 0.0,
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}
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else:
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sub_config = {
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"type": "OrnsteinUhlenbeckNoise",
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"initial_scale": 0.0, # <- no randomness whatsoever
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"final_scale": 0.0,
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"random_timesteps": 0,
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}
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config["exploration_config"] = {
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"type": "ParameterNoise",
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"sub_exploration": sub_config,
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}
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config["explore"] = True
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trainer = trainer_cls(config=config, env=env)
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# Now, when we act - even with explore=True - we would expect
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# the same action for the same input (parameter noise is
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# deterministic).
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policy = trainer.get_policy()
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policy.exploration.on_episode_start(policy, tf_sess=pol_sess)
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a_ = trainer.compute_single_action(obs)
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for _ in range(10):
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a = trainer.compute_single_action(obs, explore=True)
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check(a, a_)
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trainer.stop()
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def _get_current_noise(self, policy, fw):
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# If noise not even created yet, return 0.0.
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if policy.exploration.noise is None:
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return 0.0
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noise = policy.exploration.noise[0][0][0]
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if fw == "tf":
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noise = policy.get_session().run(noise)
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elif fw == "torch":
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noise = noise.detach().cpu().numpy()
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else:
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noise = noise.numpy()
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return noise
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def _get_current_weight(self, policy, fw):
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weights = policy.get_weights()
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if fw == "torch":
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# DQN model.
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if "_hidden_layers.0._model.0.weight" in weights:
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return weights["_hidden_layers.0._model.0.weight"][0][0]
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# DDPG model.
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else:
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return weights["policy_model.action_0._model.0.weight"][0][0]
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key = 0 if fw in ["tf2", "tfe"] else list(weights.keys())[0]
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return weights[key][0][0]
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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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