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164 lines
6.1 KiB
164 lines
6.1 KiB
import gymnasium as gym
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import minigrid
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import ray
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from ray.tune import register_env
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from ray.tune.experiment.trial import Trial
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from ray import tune, air
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from ray.rllib.algorithms.ppo import PPOConfig
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from ray.tune.logger import UnifiedLogger
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from ray.rllib.models import ModelCatalog
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from ray.tune.logger import pretty_print, UnifiedLogger, CSVLogger
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from ray.rllib.algorithms.algorithm import Algorithm
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from ray.air import session
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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, test_name
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from shieldhandlers import MiniGridShieldHandler, create_shield_query
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from torch.utils.tensorboard import SummaryWriter
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from callbacks import MyCallbacks
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def shielding_env_creater(config):
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name = config.get("name", "MiniGrid-LavaCrossingS9N3-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.expname}_{args.grid_path}_{config.worker_index}.txt"
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args.prism_path = F"{args.expname}_{args.prism_path}_{config.worker_index}.prism"
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shielding = config.get("shielding", False)
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shield_creator = MiniGridShieldHandler(grid_file=args.grid_path,
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grid_to_prism_path=args.grid_to_prism_binary_path,
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prism_path=args.prism_path,
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formula=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 ,mask_actions=shielding)
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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 trial_name_creator(trial : Trial):
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return "trial"
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def ppo(args):
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register_minigrid_shielding_env(args)
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logdir = args.log_dir
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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",
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env_config={"name": args.env,
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"args": args,
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"shielding": args.shielding is ShieldingConfig.Full or args.shielding is ShieldingConfig.Training,
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},)
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.framework("torch")
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.callbacks(MyCallbacks)
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.evaluation(evaluation_config={
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"evaluation_interval": 1,
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"evaluation_duration": 10,
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"evaluation_num_workers":1,
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"env": "mini-grid-shielding",
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"env_config": {"name": args.env,
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"args": args,
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"shielding": args.shielding is ShieldingConfig.Full or args.shielding is ShieldingConfig.Evaluation}})
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.rl_module(_enable_rl_module_api = False)
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.debugging(logger_config={
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"type": UnifiedLogger,
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"logdir": logdir
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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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tuner = tune.Tuner("PPO",
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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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trial_name_creator=trial_name_creator,
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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": args.steps,},
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checkpoint_config=air.CheckpointConfig(checkpoint_at_end=True,
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num_to_keep=1,
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checkpoint_score_attribute="episode_reward_mean",
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),
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storage_path=F"{logdir}",
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name=test_name(args),
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)
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,
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param_space=config,)
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results = tuner.fit()
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best_result = results.get_best_result()
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import pprint
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metrics_to_print = [
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"episode_reward_mean",
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"episode_reward_max",
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"episode_reward_min",
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"episode_len_mean",
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]
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pprint.pprint({k: v for k, v in best_result.metrics.items() if k in metrics_to_print})
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# algo = Algorithm.from_checkpoint(best_result.checkpoint)
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# eval_log_dir = F"{logdir}-eval"
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# writer = SummaryWriter(log_dir=eval_log_dir)
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# csv_logger = CSVLogger(config=config, logdir=eval_log_dir)
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# for i in range(args.evaluations):
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# eval_result = algo.evaluate()
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# print(pretty_print(eval_result))
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# print(eval_result)
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# # logger.on_result(eval_result)
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# csv_logger.on_result(eval_result)
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# evaluation = eval_result['evaluation']
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# epsiode_reward_mean = evaluation['episode_reward_mean']
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# episode_len_mean = evaluation['episode_len_mean']
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# print(epsiode_reward_mean)
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# writer.add_scalar("evaluation/episode_reward_mean", epsiode_reward_mean, i)
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# writer.add_scalar("evaluation/episode_len_mean", episode_len_mean, i)
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def main():
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ray.init(num_cpus=3)
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import argparse
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args = parse_arguments(argparse)
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ppo(args)
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ray.shutdown()
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if __name__ == '__main__':
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main()
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