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from sb3_contrib import MaskablePPO
from sb3_contrib.common.maskable.evaluation import evaluate_policy
from sb3_contrib.common.maskable.policies import MaskableActorCriticPolicy
from sb3_contrib.common.wrappers import ActionMasker
import gymnasium as gym
from minigrid.core.actions import Actions
import time
from utils import MiniGridShieldHandler, create_shield_query, parse_arguments, create_log_dir, ShieldingConfig
from sb3utils import MiniGridSbShieldingWrapper
def mask_fn(env: gym.Env):
return env.create_action_mask()
def main():
import argparse
args = parse_arguments(argparse)
args.grid_path = F"{args.grid_path}.txt"
args.prism_path = F"{args.prism_path}.prism"
shield_creator = MiniGridShieldHandler(args.grid_path, args.grid_to_prism_binary_path, args.prism_path, args.formula)
env = gym.make(args.env, render_mode="rgb_array")
env = MiniGridSbShieldingWrapper(env, shield_creator=shield_creator, shield_query_creator=create_shield_query, mask_actions=args.shielding == ShieldingConfig.Full)
env = ActionMasker(env, mask_fn)
model = MaskablePPO(MaskableActorCriticPolicy, env, gamma=0.4, verbose=1, tensorboard_log=create_log_dir(args))
steps = args.steps
model.learn(steps)
#W mean_reward, std_reward = evaluate_policy(model, model.get_env())
vec_env = model.get_env()
obs = vec_env.reset()
terminated = truncated = False
while not terminated and not truncated:
action_masks = None
action, _states = model.predict(obs, action_masks=action_masks)
obs, reward, terminated, truncated, info = env.step(action)
# action, _states = model.predict(obs, deterministic=True)
# obs, rewards, dones, info = vec_env.step(action)
vec_env.render("human")
time.sleep(0.2)
if __name__ == '__main__':
main()