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import gymnasium as gymimport numpy as npimport random
from minigrid.core.actions import Actionsfrom minigrid.core.constants import COLORS, OBJECT_TO_IDX, STATE_TO_IDX
from gymnasium.spaces import Dict, Boxfrom collections import dequefrom ray.rllib.utils.numpy import one_hot
from utils import get_action_index_mapping, MiniGridShieldHandler, create_shield_query, ShieldingConfig
class OneHotShieldingWrapper(gym.core.ObservationWrapper): def __init__(self, env, vector_index, framestack): super().__init__(env) self.framestack = framestack # 49=7x7 field of vision; 16=object types; 6=colors; 3=state types. # +4: Direction. self.single_frame_dim = 49 * (len(OBJECT_TO_IDX) + len(COLORS) + len(STATE_TO_IDX)) + 4 self.init_x = None self.init_y = None self.x_positions = [] self.y_positions = [] self.x_y_delta_buffer = deque(maxlen=100) self.vector_index = vector_index self.frame_buffer = deque(maxlen=self.framestack) for _ in range(self.framestack): self.frame_buffer.append(np.zeros((self.single_frame_dim,)))
self.observation_space = Dict( { "data": gym.spaces.Box(0.0, 1.0, shape=(self.single_frame_dim * self.framestack,), dtype=np.float32), "action_mask": gym.spaces.Box(0, 10, shape=(env.action_space.n,), dtype=int), } )
def observation(self, obs): # Debug output: max-x/y positions to watch exploration progress. # print(F"Initial observation in Wrapper {obs}") if self.step_count == 0: for _ in range(self.framestack): self.frame_buffer.append(np.zeros((self.single_frame_dim,))) if self.vector_index == 0: if self.x_positions: max_diff = max( np.sqrt( (np.array(self.x_positions) - self.init_x) ** 2 + (np.array(self.y_positions) - self.init_y) ** 2 ) ) self.x_y_delta_buffer.append(max_diff) print( "100-average dist travelled={}".format( np.mean(self.x_y_delta_buffer) ) ) self.x_positions = [] self.y_positions = [] self.init_x = self.agent_pos[0] self.init_y = self.agent_pos[1]
self.x_positions.append(self.agent_pos[0]) self.y_positions.append(self.agent_pos[1])
image = obs["data"] # One-hot the last dim into 16, 6, 3 one-hot vectors, then flatten. objects = one_hot(image[:, :, 0], depth=len(OBJECT_TO_IDX)) colors = one_hot(image[:, :, 1], depth=len(COLORS)) states = one_hot(image[:, :, 2], depth=len(STATE_TO_IDX))
all_ = np.concatenate([objects, colors, states], -1) all_flat = np.reshape(all_, (-1,)) direction = one_hot(np.array(self.agent_dir), depth=4).astype(np.float32) single_frame = np.concatenate([all_flat, direction]) self.frame_buffer.append(single_frame)
tmp = {"data": np.concatenate(self.frame_buffer), "action_mask": obs["action_mask"] } return tmp
class MiniGridShieldingWrapper(gym.core.Wrapper): def __init__(self, env, shield_creator : MiniGridShieldHandler, shield_query_creator, create_shield_at_reset=False, mask_actions=True): super(MiniGridShieldingWrapper, self).__init__(env) self.max_available_actions = env.action_space.n self.observation_space = Dict( { "data": env.observation_space.spaces["image"], "action_mask" : Box(0, 10, shape=(self.max_available_actions,), dtype=np.int8), } ) self.shield_creator = shield_creator self.create_shield_at_reset = False # TODO self.shield = shield_creator.create_shield(env=self.env) self.mask_actions = mask_actions self.shield_query_creator = shield_query_creator print(F"Shielding is {self.mask_actions}")
def create_action_mask(self): if not self.mask_actions: ret = np.array([1.0] * self.max_available_actions, dtype=np.int8) return ret cur_pos_str = self.shield_query_creator(self.env) # Create the mask # If shield restricts action mask only valid with 1.0 # else set all actions as valid allowed_actions = [] mask = np.array([0.0] * self.max_available_actions, dtype=np.int8)
if cur_pos_str in self.shield and self.shield[cur_pos_str]: allowed_actions = self.shield[cur_pos_str] zeroes = np.array([0.0] * len(allowed_actions), dtype=np.int8) has_allowed_actions = False
for allowed_action in allowed_actions: index = get_action_index_mapping(allowed_action.labels) # Allowed_action is a set if index is None: assert(False) allowed = 1.0 has_allowed_actions = True mask[index] = allowed else: for index, x in enumerate(mask): mask[index] = 1.0 front_tile = self.env.grid.get(self.env.front_pos[0], self.env.front_pos[1])
if front_tile is not None and front_tile.type == "key": mask[Actions.pickup] = 1.0 if front_tile and front_tile.type == "door": mask[Actions.toggle] = 1.0 # print(F"Mask is {mask} State: {cur_pos_str}") return mask
def reset(self, *, seed=None, options=None): obs, infos = self.env.reset(seed=seed, options=options) if self.create_shield_at_reset and self.mask_actions: self.shield = self.shield_creator.create_shield(env=self.env) mask = self.create_action_mask() return { "data": obs["image"], "action_mask": mask }, infos
def step(self, action): orig_obs, rew, done, truncated, info = self.env.step(action)
mask = self.create_action_mask() obs = { "data": orig_obs["image"], "action_mask": mask, }
return obs, rew, done, truncated, info
def shielding_env_creater(config): name = config.get("name", "MiniGrid-LavaCrossingS9N3-v0") framestack = config.get("framestack", 4) args = config.get("args", None) args.grid_path = F"{args.expname}_{args.grid_path}_{config.worker_index}.txt" args.prism_path = F"{args.expname}_{args.prism_path}_{config.worker_index}.prism" shielding = config.get("shielding", False) shield_creator = MiniGridShieldHandler(grid_file=args.grid_path, grid_to_prism_path=args.grid_to_prism_binary_path, prism_path=args.prism_path, formula=args.formula, shield_value=args.shield_value, prism_config=args.prism_config, shield_comparision=args.shield_comparision)
probability_intended = args.probability_intended probability_displacement = args.probability_displacement probability_turn_intended = args.probability_turn_intended probability_turn_displacement = args.probability_turn_displacement
env = gym.make(name, randomize_start=True, probability_intended=probability_intended, probability_displacement=probability_displacement, probability_turn_displacement=probability_turn_displacement, probability_turn_intended=probability_turn_intended) env = MiniGridShieldingWrapper(env, shield_creator=shield_creator, shield_query_creator=create_shield_query ,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 )
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