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217 lines
8.5 KiB
217 lines
8.5 KiB
import gymnasium as gym
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import numpy as np
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import random
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from minigrid.core.actions import Actions
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from minigrid.core.constants import COLORS, OBJECT_TO_IDX, STATE_TO_IDX
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from gymnasium.spaces import Dict, Box
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from collections import deque
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from ray.rllib.utils.numpy import one_hot
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from utils import get_action_index_mapping, MiniGridShieldHandler, create_shield_query, ShieldingConfig
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class OneHotShieldingWrapper(gym.core.ObservationWrapper):
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def __init__(self, env, vector_index, framestack):
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super().__init__(env)
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self.framestack = framestack
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# 49=7x7 field of vision; 16=object types; 6=colors; 3=state types.
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# +4: Direction.
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self.single_frame_dim = 49 * (len(OBJECT_TO_IDX) + len(COLORS) + len(STATE_TO_IDX)) + 4
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self.init_x = None
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self.init_y = None
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self.x_positions = []
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self.y_positions = []
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self.x_y_delta_buffer = deque(maxlen=100)
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self.vector_index = vector_index
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self.frame_buffer = deque(maxlen=self.framestack)
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for _ in range(self.framestack):
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self.frame_buffer.append(np.zeros((self.single_frame_dim,)))
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self.observation_space = Dict(
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{
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"data": gym.spaces.Box(0.0, 1.0, shape=(self.single_frame_dim * self.framestack,), dtype=np.float32),
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"action_mask": gym.spaces.Box(0, 10, shape=(env.action_space.n,), dtype=int),
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}
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)
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def observation(self, obs):
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# Debug output: max-x/y positions to watch exploration progress.
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# print(F"Initial observation in Wrapper {obs}")
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if self.step_count == 0:
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for _ in range(self.framestack):
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self.frame_buffer.append(np.zeros((self.single_frame_dim,)))
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if self.vector_index == 0:
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if self.x_positions:
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max_diff = max(
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np.sqrt(
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(np.array(self.x_positions) - self.init_x) ** 2
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+ (np.array(self.y_positions) - self.init_y) ** 2
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)
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)
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self.x_y_delta_buffer.append(max_diff)
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print(
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"100-average dist travelled={}".format(
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np.mean(self.x_y_delta_buffer)
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)
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)
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self.x_positions = []
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self.y_positions = []
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self.init_x = self.agent_pos[0]
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self.init_y = self.agent_pos[1]
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self.x_positions.append(self.agent_pos[0])
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self.y_positions.append(self.agent_pos[1])
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image = obs["data"]
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# One-hot the last dim into 16, 6, 3 one-hot vectors, then flatten.
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objects = one_hot(image[:, :, 0], depth=len(OBJECT_TO_IDX))
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colors = one_hot(image[:, :, 1], depth=len(COLORS))
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states = one_hot(image[:, :, 2], depth=len(STATE_TO_IDX))
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all_ = np.concatenate([objects, colors, states], -1)
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all_flat = np.reshape(all_, (-1,))
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direction = one_hot(np.array(self.agent_dir), depth=4).astype(np.float32)
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single_frame = np.concatenate([all_flat, direction])
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self.frame_buffer.append(single_frame)
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tmp = {"data": np.concatenate(self.frame_buffer), "action_mask": obs["action_mask"] }
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return tmp
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class MiniGridShieldingWrapper(gym.core.Wrapper):
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def __init__(self,
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env,
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shield_creator : MiniGridShieldHandler,
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shield_query_creator,
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create_shield_at_reset=True,
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mask_actions=True):
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super(MiniGridShieldingWrapper, self).__init__(env)
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self.max_available_actions = env.action_space.n
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self.observation_space = Dict(
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{
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"data": env.observation_space.spaces["image"],
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"action_mask" : Box(0, 10, shape=(self.max_available_actions,), dtype=np.int8),
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}
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)
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self.shield_creator = shield_creator
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self.create_shield_at_reset = create_shield_at_reset
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self.shield = shield_creator.create_shield(env=self.env)
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self.mask_actions = mask_actions
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self.shield_query_creator = shield_query_creator
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print(F"Shielding is {self.mask_actions}")
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def create_action_mask(self):
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if not self.mask_actions:
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ret = np.array([1.0] * self.max_available_actions, dtype=np.int8)
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return ret
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cur_pos_str = self.shield_query_creator(self.env)
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# Create the mask
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# If shield restricts action mask only valid with 1.0
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# else set all actions as valid
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allowed_actions = []
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mask = np.array([0.0] * self.max_available_actions, dtype=np.int8)
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if cur_pos_str in self.shield and self.shield[cur_pos_str]:
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allowed_actions = self.shield[cur_pos_str]
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zeroes = np.array([0.0] * len(allowed_actions), dtype=np.int8)
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has_allowed_actions = False
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for allowed_action in allowed_actions:
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index = get_action_index_mapping(allowed_action.labels) # Allowed_action is a set
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if index is None:
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assert(False)
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allowed = 1.0
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has_allowed_actions = True
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mask[index] = allowed
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else:
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for index, x in enumerate(mask):
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mask[index] = 1.0
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front_tile = self.env.grid.get(self.env.front_pos[0], self.env.front_pos[1])
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if front_tile is not None and front_tile.type == "key":
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mask[Actions.pickup] = 1.0
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if front_tile and front_tile.type == "door":
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mask[Actions.toggle] = 1.0
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# print(F"Mask is {mask} State: {cur_pos_str}")
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return mask
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def reset(self, *, seed=None, options=None):
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obs, infos = self.env.reset(seed=seed, options=options)
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if self.create_shield_at_reset and self.mask_actions:
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self.shield = self.shield_creator.create_shield(env=self.env)
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mask = self.create_action_mask()
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return {
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"data": obs["image"],
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"action_mask": mask
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}, infos
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def step(self, action):
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orig_obs, rew, done, truncated, info = self.env.step(action)
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mask = self.create_action_mask()
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obs = {
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"data": orig_obs["image"],
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"action_mask": mask,
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}
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return obs, rew, done, truncated, info
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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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shield_value=args.shield_value,
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prism_config=args.prism_config,
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shield_comparision=args.shield_comparision)
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probability_intended = args.probability_intended
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probability_displacement = args.probability_displacement
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probability_turn_intended = args.probability_turn_intended
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probability_turn_displacement = args.probability_turn_displacement
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env = gym.make(name,
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randomize_start=True,
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probability_intended=probability_intended,
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probability_displacement=probability_displacement,
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probability_turn_displacement=probability_turn_displacement,
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probability_turn_intended=probability_turn_intended)
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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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