import gymnasium as gym
import numpy as np
import random
from minigrid.core.actions import Actions
from minigrid.core.constants import COLORS, OBJECT_TO_IDX, STATE_TO_IDX
from gymnasium.spaces import Dict, Box
from collections import deque
from 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
)