wehub-resource-sync
131 lines
3.7 KiB
Python
131 lines
3.7 KiB
Python
import sys
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import unittest
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import numpy as np
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import ray
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import ray.rllib.algorithms.impala as impala
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import ray.rllib.algorithms.ppo as ppo
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from ray.rllib.utils import check
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def do_test_explorations(config, dummy_obs, prev_a=None, expected_mean_action=None):
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"""Calls an Agent's `compute_actions` with different `explore` options."""
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print(f"Algorithm={config.algo_class}")
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# Test for both the default Agent's exploration AND the `Random`
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# exploration class.
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for exploration in [None, "Random"]:
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local_config = config.copy()
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if exploration == "Random":
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local_config.env_runners(exploration_config={"type": "Random"})
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print("exploration={}".format(exploration or "default"))
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algo = local_config.build()
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# Make sure all actions drawn are the same, given same
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# observations.
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actions = []
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for _ in range(25):
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actions.append(
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algo.compute_single_action(
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observation=dummy_obs,
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explore=False,
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prev_action=prev_a,
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prev_reward=1.0 if prev_a is not None else None,
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)
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)
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check(actions[-1], actions[0])
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# Make sure actions drawn are different
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# (around some mean value), given constant observations.
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actions = []
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for _ in range(500):
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actions.append(
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algo.compute_single_action(
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observation=dummy_obs,
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explore=True,
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prev_action=prev_a,
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prev_reward=1.0 if prev_a is not None else None,
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)
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)
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check(
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np.mean(actions),
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expected_mean_action if expected_mean_action is not None else 0.5,
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atol=0.4,
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)
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# Check that the stddev is not 0.0 (values differ).
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check(np.std(actions), 0.0, false=True)
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class TestExplorations(unittest.TestCase):
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"""
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Tests all Exploration components and the deterministic flag for
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compute_action calls.
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"""
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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_impala(self):
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config = (
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impala.IMPALAConfig()
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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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.environment("CartPole-v1")
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.env_runners(num_env_runners=0)
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.resources(num_gpus=0)
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)
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do_test_explorations(
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config,
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np.array([0.0, 0.1, 0.0, 0.0]),
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prev_a=np.array(0),
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)
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def test_ppo_discr(self):
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config = (
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ppo.PPOConfig()
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.api_stack(
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enable_env_runner_and_connector_v2=False,
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enable_rl_module_and_learner=False,
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)
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.environment("CartPole-v1")
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.env_runners(num_env_runners=0)
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)
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do_test_explorations(
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config,
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np.array([0.0, 0.1, 0.0, 0.0]),
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prev_a=np.array(0),
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)
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def test_ppo_cont(self):
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config = (
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ppo.PPOConfig()
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.api_stack(
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enable_env_runner_and_connector_v2=False,
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enable_rl_module_and_learner=False,
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)
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.environment("Pendulum-v1")
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.env_runners(num_env_runners=0)
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)
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do_test_explorations(
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config,
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np.array([0.0, 0.1, 0.0]),
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prev_a=np.array([0.0]),
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expected_mean_action=0.0,
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)
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if __name__ == "__main__":
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import pytest
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sys.exit(pytest.main(["-v", __file__]))
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