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173 lines
6.1 KiB
173 lines
6.1 KiB
import gymnasium as gym
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import numpy as np
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from minigrid.core.actions import Actions
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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 helpers import get_action_index_mapping
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class OneHotWrapper(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; 11=object types; 6=colors; 3=state types.
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# +4: Direction.
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self.single_frame_dim = 49 * (11 + 6 + 3) + 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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# print(F"Set obersvation space to {self.observation_space}")
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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 11, 6, 3 one-hot vectors, then flatten.
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objects = one_hot(image[:, :, 0], depth=11)
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colors = one_hot(image[:, :, 1], depth=6)
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states = one_hot(image[:, :, 2], depth=3)
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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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#obs["one-hot"] = np.concatenate(self.frame_buffer)
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tmp = {"data": np.concatenate(self.frame_buffer), "action_mask": obs["action_mask"] }
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return tmp#np.concatenate(self.frame_buffer)
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class MiniGridEnvWrapper(gym.core.Wrapper):
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def __init__(self, env, shield={}, keys=[]):
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super(MiniGridEnvWrapper, 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.keys = keys
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self.shield = shield
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def create_action_mask(self):
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coordinates = self.env.agent_pos
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view_direction = self.env.agent_dir
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key_text = ""
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# only support one key for now
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if self.keys:
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key_text = F"!Agent_has_{self.keys[0]}_key\t& "
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if self.env.carrying and self.env.carrying.type == "key":
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key_text = F"Agent_has_{self.env.carrying.color}_key\t& "
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#print(F"Agent pos is {self.env.agent_pos} and direction {self.env.agent_dir} ")
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cur_pos_str = f"[{key_text}!AgentDone\t& xAgent={coordinates[0]}\t& yAgent={coordinates[1]}\t& viewAgent={view_direction}]"
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allowed_actions = []
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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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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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for allowed_action in allowed_actions:
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index = get_action_index_mapping(allowed_action[1]) # Allowed_action is a set
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if index is None:
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assert(False)
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mask[index] = 1.0
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else:
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# print(F"Not in shield {cur_pos_str}")
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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 self.env.carrying:
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mask[Actions.drop] = 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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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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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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# print(F"Performed action in step: {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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#print(F"Original observation is {orig_obs}")
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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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#print(F"Info is {info}")
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return obs, rew, done, truncated, info
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