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133 lines
4.4 KiB
133 lines
4.4 KiB
import gymnasium as gym
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import minigrid
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from ray import tune, air
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from ray.tune import register_env
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from ray.rllib.algorithms.algorithm import Algorithm
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from ray.rllib.algorithms.ppo import PPOConfig
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from ray.rllib.algorithms.dqn.dqn import DQNConfig
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from ray.tune.logger import pretty_print
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from ray.rllib.models import ModelCatalog
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from torch_action_mask_model import TorchActionMaskModel
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from wrappers import OneHotShieldingWrapper, MiniGridShieldingWrapper
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from helpers import parse_arguments, create_log_dir, ShieldingConfig
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from shieldhandlers import MiniGridShieldHandler, create_shield_query
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from callbacks import MyCallbacks
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from torch.utils.tensorboard import SummaryWriter
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from ray.tune.logger import TBXLogger, UnifiedLogger, CSVLogger
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def shielding_env_creater(config):
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name = config.get("name", "MiniGrid-LavaCrossingS9N1-v0")
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framestack = config.get("framestack", 4)
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args = config.get("args", None)
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args.grid_path = F"{args.grid_path}_{config.worker_index}.txt"
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args.prism_path = F"{args.prism_path}_{config.worker_index}.prism"
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shield_creator = MiniGridShieldHandler(args.grid_path, args.grid_to_prism_binary_path, args.prism_path, args.formula)
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env = gym.make(name)
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env = MiniGridShieldingWrapper(env, shield_creator=shield_creator, shield_query_creator=create_shield_query)
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# env = minigrid.wrappers.ImgObsWrapper(env)
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# env = ImgObsWrapper(env)
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env = OneHotShieldingWrapper(env,
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config.vector_index if hasattr(config, "vector_index") else 0,
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framestack=framestack
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)
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return env
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def register_minigrid_shielding_env(args):
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env_name = "mini-grid-shielding"
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register_env(env_name, shielding_env_creater)
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ModelCatalog.register_custom_model(
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"shielding_model",
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TorchActionMaskModel
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)
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def ppo(args):
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register_minigrid_shielding_env(args)
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config = (PPOConfig()
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.rollouts(num_rollout_workers=args.workers)
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.resources(num_gpus=0)
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.environment(env="mini-grid-shielding", env_config={"name": args.env, "args": args, "shielding": args.shielding is ShieldingConfig.Full or args.shielding is ShieldingConfig.Training})
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.framework("torch")
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.callbacks(MyCallbacks)
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.rl_module(_enable_rl_module_api = False)
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.debugging(logger_config={
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"type": TBXLogger,
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"logdir": create_log_dir(args)
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})
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.training(_enable_learner_api=False ,model={
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"custom_model": "shielding_model"
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}))
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return config
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def dqn(args):
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register_minigrid_shielding_env(args)
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config = DQNConfig()
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config = config.resources(num_gpus=0)
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config = config.rollouts(num_rollout_workers=args.workers)
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config = config.environment(env="mini-grid-shielding", env_config={"name": args.env, "args": args })
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config = config.framework("torch")
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config = config.callbacks(MyCallbacks)
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config = config.rl_module(_enable_rl_module_api = False)
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config = config.debugging(logger_config={
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"type": TBXLogger,
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"logdir": create_log_dir(args)
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})
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config = config.training(hiddens=[], dueling=False, model={
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"custom_model": "shielding_model"
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})
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return config
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def main():
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import argparse
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args = parse_arguments(argparse)
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if args.algorithm == "PPO":
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config = ppo(args)
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elif args.algorithm == "DQN":
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config = dqn(args)
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logdir = create_log_dir(args)
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tuner = tune.Tuner(args.algorithm,
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tune_config=tune.TuneConfig(
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metric="episode_reward_mean",
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mode="max",
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num_samples=1,
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),
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run_config=air.RunConfig(
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stop = {"episode_reward_mean": 94,
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"timesteps_total": 12000,
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"training_iteration": args.iterations},
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checkpoint_config=air.CheckpointConfig(checkpoint_at_end=True, num_to_keep=2 ),
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storage_path=F"{logdir}"
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),
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param_space=config,
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)
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tuner.fit()
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if __name__ == '__main__':
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main()
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