wehub-resource-sync
111 lines
3.4 KiB
Python
111 lines
3.4 KiB
Python
import random
|
|
|
|
import gymnasium as gym
|
|
|
|
from ray.rllib.algorithms.appo import APPOConfig
|
|
from ray.rllib.connectors.env_to_module.frame_stacking import FrameStackingEnvToModule
|
|
from ray.rllib.connectors.learner.frame_stacking import FrameStackingLearner
|
|
from ray.rllib.core.rl_module.default_model_config import DefaultModelConfig
|
|
from ray.rllib.core.rl_module.multi_rl_module import MultiRLModuleSpec
|
|
from ray.rllib.core.rl_module.rl_module import RLModuleSpec
|
|
from ray.rllib.env.multi_agent_env import make_multi_agent
|
|
from ray.rllib.env.wrappers.atari_wrappers import wrap_atari_for_new_api_stack
|
|
from ray.rllib.examples.rl_modules.classes.random_rlm import RandomRLModule
|
|
from ray.rllib.examples.utils import (
|
|
add_rllib_example_script_args,
|
|
run_rllib_example_script_experiment,
|
|
)
|
|
|
|
parser = add_rllib_example_script_args(
|
|
default_reward=0.0,
|
|
default_timesteps=20000000,
|
|
default_iters=400,
|
|
)
|
|
parser.set_defaults(
|
|
env="ale_py:ALE/Pong-v5",
|
|
num_agents=2,
|
|
)
|
|
args = parser.parse_args()
|
|
|
|
|
|
def _make_env_to_module_connector(env, spaces, device):
|
|
return FrameStackingEnvToModule(num_frames=4, multi_agent=True)
|
|
|
|
|
|
def _make_learner_connector(input_observation_space, input_action_space):
|
|
return FrameStackingLearner(num_frames=4, multi_agent=True)
|
|
|
|
|
|
def _env_creator(cfg):
|
|
return wrap_atari_for_new_api_stack(
|
|
gym.make(args.env, **cfg, **{"render_mode": "rgb_array"}),
|
|
dim=64,
|
|
framestack=None,
|
|
)
|
|
|
|
|
|
MultiAgentPong = make_multi_agent(_env_creator)
|
|
NUM_POLICIES = 5
|
|
main_spec = RLModuleSpec(
|
|
model_config=DefaultModelConfig(
|
|
vf_share_layers=True,
|
|
conv_filters=[(16, 4, 2), (32, 4, 2), (64, 4, 2), (128, 4, 2)],
|
|
conv_activation="relu",
|
|
head_fcnet_hiddens=[256],
|
|
),
|
|
)
|
|
|
|
|
|
config = (
|
|
APPOConfig()
|
|
.environment(
|
|
MultiAgentPong,
|
|
env_config={
|
|
"num_agents": args.num_agents,
|
|
# Make analogous to old v4 + NoFrameskip.
|
|
"frameskip": 1,
|
|
"full_action_space": False,
|
|
"repeat_action_probability": 0.0,
|
|
},
|
|
clip_rewards=True,
|
|
)
|
|
.env_runners(
|
|
env_to_module_connector=_make_env_to_module_connector,
|
|
)
|
|
.learners(
|
|
num_aggregator_actors_per_learner=2,
|
|
)
|
|
.training(
|
|
learner_connector=_make_learner_connector,
|
|
train_batch_size_per_learner=500,
|
|
target_network_update_freq=2,
|
|
lr=0.0005 * ((args.num_learners or 1) ** 0.5),
|
|
vf_loss_coeff=1.0,
|
|
entropy_coeff=[[0, 0.01], [3000000, 0.0]], # <- crucial parameter to finetune
|
|
# Only update connector states and model weights every n training_step calls.
|
|
broadcast_interval=5,
|
|
# learner_queue_size=1,
|
|
circular_buffer_num_batches=4,
|
|
circular_buffer_iterations_per_batch=2,
|
|
)
|
|
.rl_module(
|
|
rl_module_spec=MultiRLModuleSpec(
|
|
rl_module_specs=(
|
|
{f"p{i}": main_spec for i in range(NUM_POLICIES)}
|
|
| {"random": RLModuleSpec(module_class=RandomRLModule)}
|
|
),
|
|
),
|
|
)
|
|
.multi_agent(
|
|
policies={f"p{i}" for i in range(NUM_POLICIES)} | {"random"},
|
|
policy_mapping_fn=lambda aid, eps, **kw: (
|
|
random.choice([f"p{i}" for i in range(NUM_POLICIES)] + ["random"])
|
|
),
|
|
policies_to_train=[f"p{i}" for i in range(NUM_POLICIES)],
|
|
)
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
run_rllib_example_script_experiment(config, args)
|