import numpy as np from gym_minigrid.minigrid import * from gym_minigrid.register import register import time from collections import deque class Peer(NPC): """ A dancing NPC that the agent has to copy """ def __init__(self, color, name, env): super().__init__(color) self.name = name self.npc_dir = 1 # NPC initially looks downward self.npc_type = 0 self.env = env self.npc_actions = [] self.dancing_step_idx = 0 self.actions = MiniGridEnv.Actions self.add_npc_direction = True self.available_moves = [self.rotate_left, self.rotate_right, self.go_forward, self.toggle_action] selected_door_id = self.env._rand_elem([0, 1]) self.selected_door_pos = [self.env.door_pos_top, self.env.door_pos_bottom][selected_door_id] self.selected_door = [self.env.door_top, self.env.door_bottom][selected_door_id] def step(self): if all(self.front_pos == self.selected_door_pos): # in front of door if self.selected_door.is_open: self.go_forward() else: if (self.cur_pos[0] == self.selected_door_pos[0]) or (self.cur_pos[1] == self.selected_door_pos[1]): # is either in the correct row on in the correct column next_wanted_position = self.selected_door_pos else: # choose the midpoint for cand_x, cand_y in [ (self.cur_pos[0], self.selected_door_pos[1]), (self.selected_door_pos[0], self.cur_pos[1]) ]: print("wX:", self.env.wall_x) print("wY:", self.env.wall_y) if ( cand_x > 0 and cand_x < self.env.wall_x ) and ( cand_y > 0 and cand_y < self.env.wall_y ): next_wanted_position = (cand_x, cand_y) print("wanted_pos:", next_wanted_position) if self.cur_pos[1] == next_wanted_position[1]: # same y if self.cur_pos[0] < next_wanted_position[0]: wanted_dir = 0 else: wanted_dir = 2 if self.npc_dir == wanted_dir: self.go_forward() else: self.rotate_left() elif self.cur_pos[0] == next_wanted_position[0]: # same x if self.cur_pos[1] < next_wanted_position[1]: wanted_dir = 1 else: wanted_dir = 3 if self.npc_dir == wanted_dir: self.go_forward() else: self.rotate_left() else: raise ValueError("Something is wrong.") class TwoDoorsIntentGrammar(object): templates = ["Move your", "Shake your"] things = ["body", "head"] grammar_action_space = spaces.MultiDiscrete([len(templates), len(things)]) @classmethod def construct_utterance(cls, action): return cls.templates[int(action[0])] + " " + cls.things[int(action[1])] + " " class TwoDoorsIntentEnv(MultiModalMiniGridEnv): """ Environment in which the agent is instructed to go to a given object named using an English text string """ def __init__( self, size=5, diminished_reward=True, step_penalty=False, knowledgeable=False, ): assert size >= 5 self.empty_symbol = "NA \n" self.diminished_reward = diminished_reward self.step_penalty = step_penalty self.knowledgeable = knowledgeable super().__init__( grid_size=size, max_steps=5*size**2, # Set this to True for maximum speed see_through_walls=True, actions=MiniGridEnv.Actions, action_space=spaces.MultiDiscrete([ len(MiniGridEnv.Actions), *TwoDoorsIntentGrammar.grammar_action_space.nvec ]), add_npc_direction=True ) print({ "size": size, "diminished_reward": diminished_reward, "step_penalty": step_penalty, }) def _gen_grid(self, width, height): # Create the grid self.grid = Grid(width, height, nb_obj_dims=4) # Randomly vary the room width and height width = self._rand_int(5, width+1) height = self._rand_int(5, height+1) self.wall_x = width-1 self.wall_y = height-1 # Generate the surrounding walls self.grid.wall_rect(0, 0, width, height) # door top door_color_top = self._rand_elem(COLOR_NAMES) self.door_pos_top = (width-1, 1) self.door_top = Door(door_color_top) self.grid.set(*self.door_pos_top, self.door_top) # switch top self.switch_pos_top = (0, 1) self.switch_top = Switch(door_color_top, lockable_object=self.door_top) self.grid.set(*self.switch_pos_top, self.switch_top) # door bottom door_color_bottom = self._rand_elem(COLOR_NAMES) self.door_pos_bottom = (width-1, height-2) self.door_bottom = Door(door_color_bottom) self.grid.set(*self.door_pos_bottom, self.door_bottom) # switch bottom self.switch_pos_bottom = (0, height-2) self.switch_bottom = Switch(door_color_bottom, lockable_object=self.door_bottom) self.grid.set(*self.switch_pos_bottom, self.switch_bottom) # Set a randomly coloured Dancer NPC color = self._rand_elem(COLOR_NAMES) self.peer = Peer(color, "Jill", self) # Place it on the middle left side of the room peer_pos = np.array((self._rand_int(1, width - 1), self._rand_int(1, height - 1))) self.grid.set(*peer_pos, self.peer) self.peer.init_pos = peer_pos self.peer.cur_pos = peer_pos # Randomize the agent's start position and orientation self.place_agent(size=(width, height)) # Generate the mission string self.mission = 'watch dancer and repeat his moves afterwards' # Dummy beginning string self.beginning_string = "This is what you hear. \n" self.utterance = self.beginning_string # utterance appended at the end of each step self.utterance_history = "" # used for rendering self.conversation = self.utterance def step(self, action): p_action = action[0] utterance_action = action[1:] obs, reward, done, info = super().step(p_action) self.peer.step() if np.isnan(p_action): pass if p_action == self.actions.done: done = True elif all(self.agent_pos == self.door_pos_top): done = True elif all(self.agent_pos == self.door_pos_bottom): done = True elif all([self.switch_top.is_on, self.switch_bottom.is_on]): # if both switches are on no reward is given and episode ends done = True elif all(self.peer.cur_pos == self.peer.selected_door_pos): reward = self._reward() done = True # discount if self.step_penalty: reward = reward - 0.01 # fill observation with text self.append_existing_utterance_to_history() obs = self.add_utterance_to_observation(obs) self.reset_utterance() return obs, reward, done, info def _reward(self): if self.diminished_reward: return super()._reward() else: return 1.0 def render(self, *args, **kwargs): obs = super().render(*args, **kwargs) print("conversation:\n", self.conversation) print("utterance_history:\n", self.utterance_history) self.window.set_caption(self.conversation, [self.peer.name]) return obs class TwoDoorsIntent8x8Env(TwoDoorsIntentEnv): def __init__(self): super().__init__(size=8) class TwoDoorsIntent6x6Env(TwoDoorsIntentEnv): def __init__(self): super().__init__(size=6) register( id='MiniGrid-TwoDoorsIntent-5x5-v0', entry_point='gym_minigrid.envs:TwoDoorsIntentEnv' ) register( id='MiniGrid-TwoDoorsIntent-6x6-v0', entry_point='gym_minigrid.envs:TwoDoorsIntent6x6Env' ) register( id='MiniGrid-TwoDoorsIntent-8x8-v0', entry_point='gym_minigrid.envs:TwoDoorsIntent8x8Env' )