mirror of
https://github.com/vale981/ray
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134 lines
4 KiB
Python
134 lines
4 KiB
Python
import unittest
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import ray
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from ray.rllib.agents.registry import get_trainer_class
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from ray.rllib.examples.env.multi_agent import MultiAgentCartPole, \
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MultiAgentMountainCar
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from ray.rllib.utils.test_utils import framework_iterator
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from ray.tune import register_env
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def check_support_multiagent(alg, config):
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register_env("multi_agent_mountaincar",
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lambda _: MultiAgentMountainCar({"num_agents": 2}))
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register_env("multi_agent_cartpole",
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lambda _: MultiAgentCartPole({"num_agents": 2}))
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config["log_level"] = "ERROR"
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for fw in framework_iterator(config):
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if fw in ["tf2", "tfe"] and \
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alg in ["A3C", "APEX", "APEX_DDPG", "IMPALA"]:
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continue
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if alg in ["DDPG", "APEX_DDPG", "SAC"]:
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a = get_trainer_class(alg)(
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config=config, env="multi_agent_mountaincar")
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else:
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a = get_trainer_class(alg)(
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config=config, env="multi_agent_cartpole")
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print(a.train())
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a.stop()
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class TestSupportedMultiAgentPG(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_a3c_multiagent(self):
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check_support_multiagent("A3C", {
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"num_workers": 1,
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"optimizer": {
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"grads_per_step": 1
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}
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})
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def test_impala_multiagent(self):
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check_support_multiagent("IMPALA", {"num_gpus": 0})
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def test_pg_multiagent(self):
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check_support_multiagent("PG", {"num_workers": 1, "optimizer": {}})
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def test_ppo_multiagent(self):
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check_support_multiagent(
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"PPO", {
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"num_workers": 1,
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"num_sgd_iter": 1,
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"train_batch_size": 10,
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"rollout_fragment_length": 10,
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"sgd_minibatch_size": 1,
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})
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class TestSupportedMultiAgentOffPolicy(unittest.TestCase):
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@classmethod
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def setUpClass(cls) -> None:
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ray.init(num_cpus=6)
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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_apex_multiagent(self):
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check_support_multiagent(
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"APEX", {
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"num_workers": 2,
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"timesteps_per_iteration": 100,
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"num_gpus": 0,
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"buffer_size": 1000,
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"min_iter_time_s": 1,
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"learning_starts": 10,
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"target_network_update_freq": 100,
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"optimizer": {
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"num_replay_buffer_shards": 1,
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},
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})
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def test_apex_ddpg_multiagent(self):
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check_support_multiagent(
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"APEX_DDPG", {
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"num_workers": 2,
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"timesteps_per_iteration": 100,
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"buffer_size": 1000,
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"num_gpus": 0,
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"min_iter_time_s": 1,
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"learning_starts": 10,
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"target_network_update_freq": 100,
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"use_state_preprocessor": True,
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})
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def test_ddpg_multiagent(self):
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check_support_multiagent(
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"DDPG", {
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"timesteps_per_iteration": 1,
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"buffer_size": 1000,
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"use_state_preprocessor": True,
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"learning_starts": 500,
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})
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def test_dqn_multiagent(self):
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check_support_multiagent("DQN", {
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"timesteps_per_iteration": 1,
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"buffer_size": 1000,
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})
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def test_sac_multiagent(self):
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check_support_multiagent("SAC", {
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"num_workers": 0,
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"buffer_size": 1000,
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"normalize_actions": False,
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})
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if __name__ == "__main__":
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import pytest
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import sys
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# One can specify the specific TestCase class to run.
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# None for all unittest.TestCase classes in this file.
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class_ = sys.argv[1] if len(sys.argv) > 1 else None
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sys.exit(
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pytest.main(
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["-v", __file__ + ("" if class_ is None else "::" + class_)]))
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