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