from gym_minigrid.minigrid import * from gym_minigrid.register import register class Guide(NPC): """ A simple NPC that wants an agent to go to an object (randomly chosen among object_pos list) """ def __init__(self, color, name, env): super().__init__(color) self.name = name self.env = env self.npc_type = 0 def listen(self, utterance): if utterance == TalkHardSesameGrammar.construct_utterance([0, 1]): return self.env.mission return None def is_near_agent(self): ax, ay = self.env.agent_pos wx, wy = self.cur_pos if (ax == wx and abs(ay - wy) == 1) or (ay == wy and abs(ax - wx) == 1): return True return False class TalkHardSesameGrammar(object): templates = ["Where is", "Open"] things = ["sesame", "the exit"] 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 GoToDoorTalkHardSesameNPCEnv(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, hear_yourself=False, diminished_reward=True, step_penalty=False ): assert size >= 5 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), *TalkHardSesameGrammar.grammar_action_space.nvec ]) ) self.hear_yourself = hear_yourself self.diminished_reward = diminished_reward self.step_penalty = step_penalty self.empty_symbol = "NA \n" print({ "size": size, "hear_yourself": hear_yourself, "diminished_reward": diminished_reward, "step_penalty": step_penalty, }) def _gen_grid(self, width, height): # Create the grid self.grid = Grid(width, height) # Randomly vary the room width and height width = self._rand_int(5, width+1) height = self._rand_int(5, height+1) # Generate the surrounding walls self.grid.wall_rect(0, 0, width, height) # Generate the surrounding walls self.grid.wall_rect(0, 0, width, height) # Generate the 4 doors at random positions self.door_pos = [] self.door_front_pos = [] # Remembers positions in front of door to avoid setting wizard here self.door_pos.append((self._rand_int(2, width-2), 0)) self.door_front_pos.append((self.door_pos[-1][0], self.door_pos[-1][1]+1)) self.door_pos.append((self._rand_int(2, width-2), height-1)) self.door_front_pos.append((self.door_pos[-1][0], self.door_pos[-1][1] - 1)) self.door_pos.append((0, self._rand_int(2, height-2))) self.door_front_pos.append((self.door_pos[-1][0] + 1, self.door_pos[-1][1])) self.door_pos.append((width-1, self._rand_int(2, height-2))) self.door_front_pos.append((self.door_pos[-1][0] - 1, self.door_pos[-1][1])) # Generate the door colors self.door_colors = [] while len(self.door_colors) < len(self.door_pos): color = self._rand_elem(COLOR_NAMES) if color in self.door_colors: continue self.door_colors.append(color) # Place the doors in the grid for idx, pos in enumerate(self.door_pos): color = self.door_colors[idx] self.grid.set(*pos, Door(color)) # Set a randomly coloured NPC at a random position color = self._rand_elem(COLOR_NAMES) self.wizard = Guide(color, "Gandalf", self) # Place it randomly, omitting front of door positions self.place_obj(self.wizard, size=(width, height), reject_fn=lambda _, p: tuple(p) in self.door_front_pos) # Randomize the agent start position and orientation self.place_agent(size=(width, height)) # Select a random target door self.doorIdx = self._rand_int(0, len(self.door_pos)) self.target_pos = self.door_pos[self.doorIdx] self.target_color = self.door_colors[self.doorIdx] # Generate the mission string self.mission = 'go to the %s door' % self.target_color # 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 = "" self.conversation = self.utterance def step(self, action): p_action = action[0] utterance_action = action[1:] # assert all nan or neither nan assert len(set(np.isnan(utterance_action))) == 1 speak_flag = not all(np.isnan(utterance_action)) obs, reward, done, info = super().step(p_action) if speak_flag: utterance = TalkHardSesameGrammar.construct_utterance(utterance_action) if self.hear_yourself: self.utterance += "YOU: {} \n".format(utterance) self.conversation += "YOU: {} \n".format(utterance) # check if near wizard if self.wizard.is_near_agent(): reply = self.wizard.listen(utterance) if reply: self.utterance += "{}: {} \n".format(self.wizard.name, reply) self.conversation += "{}: {} \n".format(self.wizard.name, reply) if utterance == TalkHardSesameGrammar.construct_utterance([1, 0]): ax, ay = self.agent_pos tx, ty = self.target_pos if (ax == tx and abs(ay - ty) == 1) or (ay == ty and abs(ax - tx) == 1): reward = self._reward() for dx, dy in self.door_pos: if (ax == dx and abs(ay - dy) == 1) or (ay == dy and abs(ax - dx) == 1): # agent has chosen some door episode, regardless of if the door is correct the episode is over done = True # Don't let the agent open any of the doors if p_action == self.actions.toggle: done = True if p_action == self.actions.done: done = True # discount if self.step_penalty: reward = reward - 0.01 # fill observation with text # 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) self.window.set_caption(self.conversation, [ "Gandalf:", "Jack:", "John:", "Where is the exit", "Open sesame", ]) return obs class GoToDoorTalkHardSesameNPCTesting(GoToDoorTalkHardSesameNPCEnv): def __init__(self): super().__init__( size=5, hear_yourself=False, diminished_reward=False, step_penalty=True ) class GoToDoorTalkHardSesameNPC8x8Env(GoToDoorTalkHardSesameNPCEnv): def __init__(self): super().__init__(size=8) class GoToDoorTalkHardSesameNPC6x6Env(GoToDoorTalkHardSesameNPCEnv): def __init__(self): super().__init__(size=6) # hear yourself class GoToDoorTalkHardSesameNPCHY8x8Env(GoToDoorTalkHardSesameNPCEnv): def __init__(self): super().__init__(size=8, hear_yourself=True) class GoToDoorTalkHardSesameNPCHY6x6Env(GoToDoorTalkHardSesameNPCEnv): def __init__(self): super().__init__(size=6, hear_yourself=True) class GoToDoorTalkHardSesameNPCHY5x5Env(GoToDoorTalkHardSesameNPCEnv): def __init__(self): super().__init__(size=5, hear_yourself=True) register( id='MiniGrid-GoToDoorTalkHardSesameNPC-Testing-v0', entry_point='gym_minigrid.envs:GoToDoorTalkHardSesameNPCTesting' ) register( id='MiniGrid-GoToDoorTalkHardSesameNPC-5x5-v0', entry_point='gym_minigrid.envs:GoToDoorTalkHardSesameNPCEnv' ) register( id='MiniGrid-GoToDoorTalkHardSesameNPC-6x6-v0', entry_point='gym_minigrid.envs:GoToDoorTalkHardSesameNPC6x6Env' ) register( id='MiniGrid-GoToDoorTalkHardSesameNPC-8x8-v0', entry_point='gym_minigrid.envs:GoToDoorTalkHardSesameNPC8x8Env' ) register( id='MiniGrid-GoToDoorTalkHardSesameNPCHY-5x5-v0', entry_point='gym_minigrid.envs:GoToDoorTalkHardSesameNPCHY5x5Env' ) register( id='MiniGrid-GoToDoorTalkHardSesameNPCHY-6x6-v0', entry_point='gym_minigrid.envs:GoToDoorTalkHardSesameNPCHY6x6Env' ) register( id='MiniGrid-GoToDoorTalkHardSesameNPCHY-8x8-v0', entry_point='gym_minigrid.envs:GoToDoorTalkHardSesameNPCHY8x8Env' )