wehub-resource-sync
275 lines
9.0 KiB
Python
275 lines
9.0 KiB
Python
import unittest
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import ray
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import ray.rllib.algorithms.appo as appo
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from ray.rllib.algorithms.impala.impala import LEARNER_RESULTS_CURR_ENTROPY_COEFF_KEY
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from ray.rllib.core.rl_module.default_model_config import DefaultModelConfig
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from ray.rllib.policy.sample_batch import DEFAULT_POLICY_ID
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from ray.rllib.utils.metrics import (
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ENV_RUNNER_RESULTS,
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LEARNER_RESULTS,
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NUM_ENV_STEPS_SAMPLED_LIFETIME,
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)
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from ray.rllib.utils.test_utils import (
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check_compute_single_action,
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check_train_results,
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check_train_results_new_api_stack,
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)
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class TestAPPO(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_appo_compilation(self):
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"""Test whether APPO can be built with both frameworks."""
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config = (
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appo.APPOConfig().environment("CartPole-v1").env_runners(num_env_runners=1)
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)
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algo = config.build()
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num_iterations = 2
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for i in range(num_iterations):
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results = algo.train()
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print(results)
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check_train_results_new_api_stack(results)
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algo.stop()
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def test_appo_compilation_use_kl_loss(self):
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"""Test whether APPO can be built with kl_loss enabled."""
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config = (
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appo.APPOConfig().env_runners(num_env_runners=1).training(use_kl_loss=True)
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)
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num_iterations = 2
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algo = config.build(env="CartPole-v1")
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for i in range(num_iterations):
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results = algo.train()
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print(results)
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check_train_results_new_api_stack(results)
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algo.stop()
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def test_appo_two_optimizers_two_lrs(self):
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# Not explicitly setting this should cause a warning, but not fail.
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# config["_tf_policy_handles_more_than_one_loss"] = True
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config = (
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appo.APPOConfig()
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.api_stack(
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enable_rl_module_and_learner=False,
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enable_env_runner_and_connector_v2=False,
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)
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.env_runners(num_env_runners=1)
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.training(
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_separate_vf_optimizer=True,
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_lr_vf=0.002,
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# Make sure we have two completely separate models for policy and
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# value function.
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model={
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"vf_share_layers": False,
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},
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)
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)
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num_iterations = 2
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# Only supported for tf so far.
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algo = config.build(env="CartPole-v1")
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for i in range(num_iterations):
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results = algo.train()
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check_train_results(results)
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print(results)
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check_compute_single_action(algo)
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algo.stop()
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def test_appo_entropy_coeff_schedule(self):
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config = (
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appo.APPOConfig()
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.environment("CartPole-v1")
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.env_runners(
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num_env_runners=1,
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rollout_fragment_length=10,
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)
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.training(
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train_batch_size_per_learner=20,
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entropy_coeff=[
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[0, 0.1],
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[50000, 0.01],
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],
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)
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.reporting(
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min_train_timesteps_per_iteration=20,
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# 0 metrics reporting delay, this makes sure timestep,
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# which entropy coeff depends on, is updated after each worker rollout.
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min_time_s_per_iteration=0,
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)
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)
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def _step_n_times(algo, n: int):
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"""Step Algorithm n times.
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Returns:
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learning rate at the end of the execution.
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"""
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for _ in range(n):
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results = algo.train()
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print(results)
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return results[LEARNER_RESULTS][DEFAULT_POLICY_ID][
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LEARNER_RESULTS_CURR_ENTROPY_COEFF_KEY
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]
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algo = config.build()
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coeff = _step_n_times(algo, 10)
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# Should be close to the starting coeff of 0.01.
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ts_sampled = algo.metrics.peek(
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(ENV_RUNNER_RESULTS, NUM_ENV_STEPS_SAMPLED_LIFETIME)
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)
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expected_coeff = 0.1 - ((0.1 - 0.01) / 50000 * ts_sampled)
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self.assertLessEqual(coeff, expected_coeff + 0.005)
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self.assertGreaterEqual(coeff, expected_coeff - 0.005)
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coeff = _step_n_times(algo, 20)
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ts_sampled = algo.metrics.peek(
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(ENV_RUNNER_RESULTS, NUM_ENV_STEPS_SAMPLED_LIFETIME)
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)
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expected_coeff = 0.1 - ((0.1 - 0.01) / 50000 * ts_sampled)
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self.assertLessEqual(coeff, expected_coeff + 0.005)
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self.assertGreaterEqual(coeff, expected_coeff - 0.005)
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algo.stop()
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def test_appo_learning_rate_schedule(self):
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config = (
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appo.APPOConfig()
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.env_runners(
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num_env_runners=1,
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batch_mode="truncate_episodes",
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rollout_fragment_length=10,
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)
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.training(
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train_batch_size_per_learner=20,
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entropy_coeff=0.01,
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# Setup lr schedule for testing.
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lr=[[0, 5e-2], [50000, 0.0]],
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)
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.reporting(
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min_train_timesteps_per_iteration=20,
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# 0 metrics reporting delay, this makes sure timestep,
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# which entropy coeff depends on, is updated after each worker rollout.
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min_time_s_per_iteration=0,
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)
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)
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def _step_n_times(algo, n: int):
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"""Step Algorithm n times.
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Returns:
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learning rate at the end of the execution.
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"""
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for _ in range(n):
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results = algo.train()
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print(results)
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return results[LEARNER_RESULTS][DEFAULT_POLICY_ID][
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"default_optimizer_learning_rate"
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]
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algo = config.build(env="CartPole-v1")
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lr1 = _step_n_times(algo, 10)
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lr2 = _step_n_times(algo, 10)
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self.assertGreater(lr1, lr2)
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algo.stop()
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def test_appo_model_variables(self):
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config = (
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appo.APPOConfig()
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.environment("CartPole-v1")
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.env_runners(
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num_env_runners=1,
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rollout_fragment_length=10,
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)
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.training(
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train_batch_size_per_learner=20,
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)
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.rl_module(
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model_config=DefaultModelConfig(
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fcnet_hiddens=[16],
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vf_share_layers=True,
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),
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)
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)
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algo = config.build()
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state = algo.get_module(DEFAULT_POLICY_ID).get_state()
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# Weights and biases for the encoder hidden layer (2) and the output layer
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# of the policy (2), plus the `log_std_clip` param (1), makes 5 altogether.
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# We should not get the tensors from the target model here or any value function
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# related parameters (inference-only).
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self.assertEqual(len(state), 5)
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def test_env_runner_state_server_on_vs_off(self):
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"""PULL-based EnvRunnerStateServer: APPO learns with the flag ON and OFF.
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Also checks the global server actor is created only when the flag is enabled.
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"""
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for use_server in [False, True]:
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print(f"Testing with use_server={use_server}")
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config = (
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appo.APPOConfig()
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.environment("CartPole-v1")
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.env_runners(
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num_env_runners=2,
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use_env_runner_state_server=use_server,
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)
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)
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algo = config.build()
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# The global server actor exists iff the flag is enabled.
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self.assertEqual(algo._env_runner_state_server is not None, use_server)
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results = algo.train()
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check_train_results_new_api_stack(results)
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algo.stop()
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def test_env_runner_state_server_kill_and_recover(self):
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"""Killing the EnvRunnerStateServer must not stop training; it recovers."""
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config = (
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appo.APPOConfig()
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.environment("CartPole-v1")
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.env_runners(num_env_runners=2, use_env_runner_state_server=True)
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)
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algo = config.build()
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self.assertIsNotNone(algo._env_runner_state_server)
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for _ in range(3):
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algo.train()
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version_before = ray.get(algo._env_runner_state_server.get_version.remote())
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self.assertGreater(version_before, 0)
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# Kill the server. `max_restarts=-1` makes Ray restart it (with empty state).
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ray.kill(algo._env_runner_state_server, no_restart=False)
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# Training continues through the gap and the next push re-seeds the server.
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for _ in range(3):
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results = algo.train()
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check_train_results_new_api_stack(results)
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version_after = ray.get(algo._env_runner_state_server.get_version.remote())
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self.assertGreaterEqual(version_after, version_before)
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algo.stop()
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if __name__ == "__main__":
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import sys
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import pytest
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sys.exit(pytest.main(["-v", __file__]))
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