mirror of
https://github.com/vale981/ray
synced 2025-03-10 13:26:39 -04:00
105 lines
3.1 KiB
Python
105 lines
3.1 KiB
Python
|
import unittest
|
||
|
|
|
||
|
|
import ray
|
||
|
|
from ray.rllib.agents.registry import get_agent_class
|
||
|
|
from ray.rllib.examples.env.multi_agent import MultiAgentCartPole, \
|
||
|
|
MultiAgentMountainCar
|
||
|
|
from ray.rllib.utils.test_utils import framework_iterator
|
||
|
|
from ray.tune import register_env
|
||
|
|
|
||
|
|
|
||
|
|
def check_support_multiagent(alg, config):
|
||
|
|
register_env("multi_agent_mountaincar",
|
||
|
|
lambda _: MultiAgentMountainCar({"num_agents": 2}))
|
||
|
|
register_env("multi_agent_cartpole",
|
||
|
|
lambda _: MultiAgentCartPole({"num_agents": 2}))
|
||
|
|
config["log_level"] = "ERROR"
|
||
|
|
for _ in framework_iterator(config, frameworks=("tf", "torch")):
|
||
|
|
if alg in ["DDPG", "APEX_DDPG", "SAC"]:
|
||
|
|
a = get_agent_class(alg)(
|
||
|
|
config=config, env="multi_agent_mountaincar")
|
||
|
|
else:
|
||
|
|
a = get_agent_class(alg)(config=config, env="multi_agent_cartpole")
|
||
|
|
try:
|
||
|
|
a.train()
|
||
|
|
finally:
|
||
|
|
a.stop()
|
||
|
|
|
||
|
|
|
||
|
|
class ModelSupportedSpaces(unittest.TestCase):
|
||
|
|
def setUp(self):
|
||
|
|
ray.init(num_cpus=4, ignore_reinit_error=True)
|
||
|
|
|
||
|
|
def tearDown(self):
|
||
|
|
ray.shutdown()
|
||
|
|
|
||
|
|
def test_a3c_multiagent(self):
|
||
|
|
check_support_multiagent("A3C", {
|
||
|
|
"num_workers": 1,
|
||
|
|
"optimizer": {
|
||
|
|
"grads_per_step": 1
|
||
|
|
}
|
||
|
|
})
|
||
|
|
|
||
|
|
def test_apex_multiagent(self):
|
||
|
|
check_support_multiagent(
|
||
|
|
"APEX", {
|
||
|
|
"num_workers": 2,
|
||
|
|
"timesteps_per_iteration": 1000,
|
||
|
|
"num_gpus": 0,
|
||
|
|
"min_iter_time_s": 1,
|
||
|
|
"learning_starts": 1000,
|
||
|
|
"target_network_update_freq": 100,
|
||
|
|
})
|
||
|
|
|
||
|
|
def test_apex_ddpg_multiagent(self):
|
||
|
|
check_support_multiagent(
|
||
|
|
"APEX_DDPG", {
|
||
|
|
"num_workers": 2,
|
||
|
|
"timesteps_per_iteration": 1000,
|
||
|
|
"num_gpus": 0,
|
||
|
|
"min_iter_time_s": 1,
|
||
|
|
"learning_starts": 1000,
|
||
|
|
"target_network_update_freq": 100,
|
||
|
|
"use_state_preprocessor": True,
|
||
|
|
})
|
||
|
|
|
||
|
|
def test_ddpg_multiagent(self):
|
||
|
|
check_support_multiagent(
|
||
|
|
"DDPG", {
|
||
|
|
"timesteps_per_iteration": 1,
|
||
|
|
"use_state_preprocessor": True,
|
||
|
|
"learning_starts": 500,
|
||
|
|
})
|
||
|
|
|
||
|
|
def test_dqn_multiagent(self):
|
||
|
|
check_support_multiagent("DQN", {"timesteps_per_iteration": 1})
|
||
|
|
|
||
|
|
def test_impala_multiagent(self):
|
||
|
|
check_support_multiagent("IMPALA", {"num_gpus": 0})
|
||
|
|
|
||
|
|
def test_pg_multiagent(self):
|
||
|
|
check_support_multiagent("PG", {"num_workers": 1, "optimizer": {}})
|
||
|
|
|
||
|
|
def test_ppo_multiagent(self):
|
||
|
|
check_support_multiagent(
|
||
|
|
"PPO", {
|
||
|
|
"num_workers": 1,
|
||
|
|
"num_sgd_iter": 1,
|
||
|
|
"train_batch_size": 10,
|
||
|
|
"rollout_fragment_length": 10,
|
||
|
|
"sgd_minibatch_size": 1,
|
||
|
|
})
|
||
|
|
|
||
|
|
def test_sac_multiagent(self):
|
||
|
|
check_support_multiagent("SAC", {
|
||
|
|
"num_workers": 0,
|
||
|
|
"normalize_actions": False,
|
||
|
|
})
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
import pytest
|
||
|
|
import sys
|
||
|
|
sys.exit(pytest.main(["-v", __file__]))
|