You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
114 lines
3.3 KiB
114 lines
3.3 KiB
|
|
import gymnasium as gym
|
|
|
|
import minigrid
|
|
# import numpy as np
|
|
|
|
# import ray
|
|
from ray.tune import register_env
|
|
from ray.rllib.algorithms.ppo import PPOConfig
|
|
from ray.rllib.algorithms.dqn.dqn import DQNConfig
|
|
# from ray.rllib.algorithms.callbacks import DefaultCallbacks
|
|
from ray.tune.logger import pretty_print
|
|
from ray.rllib.models import ModelCatalog
|
|
|
|
|
|
from TorchActionMaskModel import TorchActionMaskModel
|
|
from Wrappers import OneHotShieldingWrapper, MiniGridShieldingWrapper
|
|
from helpers import parse_arguments, create_log_dir, ShieldingConfig
|
|
from ShieldHandlers import MiniGridShieldHandler
|
|
|
|
import matplotlib.pyplot as plt
|
|
|
|
from ray.tune.logger import TBXLogger
|
|
|
|
|
|
|
|
def shielding_env_creater(config):
|
|
name = config.get("name", "MiniGrid-LavaCrossingS9N1-v0")
|
|
framestack = config.get("framestack", 4)
|
|
args = config.get("args", None)
|
|
args.grid_path = F"{args.grid_path}_{config.worker_index}.txt"
|
|
args.prism_path = F"{args.prism_path}_{config.worker_index}.prism"
|
|
|
|
shielding = config.get("shielding", False)
|
|
|
|
# if shielding:
|
|
# assert(False)
|
|
|
|
shield_creator = MiniGridShieldHandler(args.grid_path, args.grid_to_prism_binary_path, args.prism_path, args.formula)
|
|
|
|
env = gym.make(name)
|
|
env = MiniGridShieldingWrapper(env, shield_creator=shield_creator, mask_actions=shielding)
|
|
|
|
env = OneHotShieldingWrapper(env,
|
|
config.vector_index if hasattr(config, "vector_index") else 0,
|
|
framestack=framestack
|
|
)
|
|
|
|
|
|
return env
|
|
|
|
|
|
def register_minigrid_shielding_env(args):
|
|
env_name = "mini-grid-shielding"
|
|
register_env(env_name, shielding_env_creater)
|
|
|
|
ModelCatalog.register_custom_model(
|
|
"shielding_model",
|
|
TorchActionMaskModel
|
|
)
|
|
|
|
|
|
def ppo(args):
|
|
register_minigrid_shielding_env(args)
|
|
|
|
config = (PPOConfig()
|
|
.rollouts(num_rollout_workers=args.workers)
|
|
.resources(num_gpus=0)
|
|
.environment( env="mini-grid-shielding",
|
|
env_config={"name": args.env, "args": args, "shielding": args.shielding is ShieldingConfig.Enabled or args.shielding is ShieldingConfig.Training})
|
|
.framework("torch")
|
|
.evaluation(evaluation_config={ "evaluation_interval": 1,
|
|
"evaluation_parallel_to_training": False,
|
|
"env": "mini-grid-shielding",
|
|
"env_config": {"name": args.env, "args": args, "shielding": args.shielding is ShieldingConfig.Enabled or args.shielding is ShieldingConfig.Evaluation}})
|
|
#.callbacks(MyCallbacks)
|
|
.rl_module(_enable_rl_module_api = False)
|
|
.debugging(logger_config={
|
|
"type": TBXLogger,
|
|
"logdir": create_log_dir(args)
|
|
})
|
|
.training(_enable_learner_api=False ,model={
|
|
"custom_model": "shielding_model"
|
|
}))
|
|
|
|
algo =(
|
|
|
|
config.build()
|
|
)
|
|
|
|
iterations = args.iterations
|
|
|
|
for i in range(iterations):
|
|
algo.train()
|
|
|
|
if i % 5 == 0:
|
|
algo.save()
|
|
|
|
|
|
for i in range(iterations):
|
|
eval_result = algo.evaluate()
|
|
print(pretty_print(eval_result))
|
|
|
|
|
|
def main():
|
|
import argparse
|
|
args = parse_arguments(argparse)
|
|
|
|
ppo(args)
|
|
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|