def regression_test(local_mode=False):
num_agents = 3
local_dir = tempfile.mkdtemp()
initialize_ray(test_mode=True, local_mode=local_mode)
train(DiCESACTrainer, {
"gamma":
0.95,
"target_network_update_freq":
32,
"tau":
1.0,
"train_batch_size":
200,
"rollout_fragment_length":
50,
"optimization": {
"actor_learning_rate": 0.005,
"critic_learning_rate": 0.005,
"entropy_learning_rate": 0.0001
},
**get_marl_env_config("CartPole-v0",
num_agents,
normalize_actions=False)
}, {"episode_reward_mean": 150 * num_agents},
exp_name="DELETEME",
local_dir=local_dir,
test_mode=True)
shutil.rmtree(local_dir, ignore_errors=True)
def dice_sac_trainer():
initialize_ray(test_mode=True, local_mode=True)
env_name = "BipedalWalker-v2"
num_agents = 3
env = gym.make(env_name)
trainer = DiCESACTrainer(get_marl_env_config(env_name,
num_agents,
normalize_actions=False),
env=MultiAgentEnvWrapper)
return env, trainer
def regression_test2(local_mode=False):
from ray import tune
num_agents = 3
local_dir = tempfile.mkdtemp()
initialize_ray(test_mode=True, local_mode=local_mode)
train(
DiCESACTrainer,
{
"soft_horizon":
True,
"clip_actions":
False,
"normalize_actions":
False, # <<== Handle in MARL env
"metrics_smoothing_episodes":
5,
"no_done_at_end":
True,
"train_batch_size":
1000,
"rollout_fragment_length":
50,
constants.DELAY_UPDATE:
tune.grid_search([True, False]),
# constants.NOR: tune.grid_search([True, False]),
# "optimization": {
# "actor_learning_rate": 0.005,
# "critic_learning_rate": 0.005,
# "entropy_learning_rate": 0.0001
# },
**get_marl_env_config("Pendulum-v0",
num_agents,
normalize_actions=True)
},
{
"episode_reward_mean": -300 * num_agents,
"timesteps_total": 13000 * num_agents
},
exp_name="DELETEME",
local_dir=local_dir,
test_mode=True)
shutil.rmtree(local_dir, ignore_errors=True)
stop = int(5e7)
config = {
"num_sgd_iter": 10,
"num_envs_per_worker": 1,
"entropy_coeff": 0.001,
"lambda": 0.95,
"lr": 2.5e-4,
# 'sample_batch_size': 200 if large else 50,
# 'sgd_minibatch_size': 100 if large else 64,
# 'train_batch_size': 10000 if large else 2048,
"num_gpus": 1,
"num_cpus_per_worker": 1,
"num_cpus_for_driver": 2,
'num_workers': 16
}
config.update(
get_marl_env_config(env_name, tune.grid_search([args.num_agents])))
train(
DiCETrainer,
config=config,
stop=stop,
exp_name=exp_name,
num_seeds=args.num_seeds,
num_gpus=args.num_gpus,
test_mode=args.test,
)
1,
"timesteps_per_iteration":
1000,
"learning_starts":
10000,
"clip_actions":
True,
# "normalize_actions": True, <<== This is handled by MARL env
"evaluation_interval":
1,
"metrics_smoothing_episodes":
5,
}
config.update(
get_marl_env_config(config["env"],
args.num_agents,
normalize_actions=True))
config["evaluation_config"] = dict(
# env_config=config["env_config"],
explore=False)
train(DiCESACTrainer,
config=config,
stop=stop,
exp_name=exp_name,
num_seeds=args.num_seeds,
num_gpus=args.num_gpus,
test_mode=args.test,
keep_checkpoints_num=5)
dice_utils.TWO_SIDE_CLIP_LOSS: False,
dice_utils.ONLY_TNB: True,
dice_utils.NORMALIZE_ADVANTAGE: True, # May be need to set false
}
)
DiESTrainer = DiCETrainer.with_updates(
name="DiES",
default_config=dies_default_config,
after_train_result=run_evolution_strategies
)
if __name__ == '__main__':
env_name = "CartPole-v0"
num_agents = 3
config = {
"num_sgd_iter": 2,
"train_batch_size": 400,
"update_steps": 1000,
**get_marl_env_config(env_name, num_agents)
}
initialize_ray(test_mode=True, local_mode=True)
train(
DiESTrainer,
config,
exp_name="DELETE_ME_TEST",
stop={"timesteps_total": 10000},
test_mode=True
)
