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] self.joint_attention_achieved = False def can_overlap(self): # If the NPC is hidden, agent can overlap on it return self.env.hidden_npc def encode(self, nb_dims=3): if self.env.hidden_npc: if nb_dims == 3: return (1, 0, 0) elif nb_dims == 4: return (1, 0, 0, 0) else: return super().encode(nb_dims=nb_dims) def step(self): distance_to_door = np.abs(self.selected_door_pos - self.cur_pos).sum(-1) if all(self.front_pos == self.selected_door_pos) and self.selected_door.is_open: # in front of door self.go_forward() elif distance_to_door == 1 and not self.joint_attention_achieved: # before turning to the door look at the agent wanted_dir = self.compute_wanted_dir(self.env.agent_pos) act = self.compute_turn_action(wanted_dir) act() if self.is_eye_contact(): self.joint_attention_achieved = True else: act = self.path_to_toggle_pos(self.selected_door_pos) act() # not really important as the NPC doesn't speak if self.env.hidden_npc: return None class HelperGrammar(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 HelperEnv(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, max_steps=20, hidden_npc=False, ): assert size >= 5 self.empty_symbol = "NA \n" self.diminished_reward = diminished_reward self.step_penalty = step_penalty self.knowledgeable = knowledgeable self.hidden_npc = hidden_npc super().__init__( grid_size=size, max_steps=max_steps, # Set this to True for maximum speed see_through_walls=True, actions=MiniGridEnv.Actions, action_space=spaces.MultiDiscrete([ len(MiniGridEnv.Actions), *HelperGrammar.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) # add lava self.grid.vert_wall(width//2, 1, height - 2, Lava) # door top door_color_top = self._rand_elem(COLOR_NAMES) self.door_pos_top = (width-1, 1) self.door_top = Door(door_color_top, is_locked=True) 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, locker_switch=True) 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, is_locked=True) 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, locker_switch=True) self.grid.set(*self.switch_pos_bottom, self.switch_bottom) # save to variables self.switches = [self.switch_top, self.switch_bottom] self.switches_pos = [self.switch_pos_top, self.switch_pos_bottom] self.door = [self.door_top, self.door_bottom] self.door_pos = [self.door_pos_top, self.door_pos_bottom] # Set a randomly coloured Dancer NPC color = self._rand_elem(COLOR_NAMES) self.peer = Peer(color, "Jill", self) # Place it on the middle right side of the room peer_pos = np.array((self._rand_int(width//2+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//2, 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 self.outcome_info = None 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 if self.hidden_npc: # all npc are hidden assert np.argwhere(obs['image'][:,:,0] == OBJECT_TO_IDX['npc']).size == 0 assert "{}:".format(self.peer.name) not in self.utterance # fill observation with text self.append_existing_utterance_to_history() obs = self.add_utterance_to_observation(obs) self.reset_utterance() if done: if reward > 0: self.outcome_info = "SUCCESS: agent got {} reward \n".format(np.round(reward, 1)) else: self.outcome_info = "FAILURE: agent got {} reward \n".format(reward) 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.clear_text() # erase previous text # self.window.set_caption(self.conversation, [self.peer.name]) # self.window.ax.set_title("correct door: {}".format(self.true_guide.target_color), loc="left", fontsize=10) if self.outcome_info: color = None if "SUCCESS" in self.outcome_info: color = "lime" elif "FAILURE" in self.outcome_info: color = "red" self.window.add_text(*(0.01, 0.85, self.outcome_info), **{'fontsize':15, 'color':color, 'weight':"bold"}) self.window.show_img(obs) # re-draw image to add changes to window return obs class Helper8x8Env(HelperEnv): def __init__(self, **kwargs): super().__init__(size=8, max_steps=20, **kwargs) class Helper6x6Env(HelperEnv): def __init__(self): super().__init__(size=6, max_steps=20) register( id='MiniGrid-Helper-5x5-v0', entry_point='gym_minigrid.envs:HelperEnv' ) register( id='MiniGrid-Helper-6x6-v0', entry_point='gym_minigrid.envs:Helper6x6Env' ) register( id='MiniGrid-Helper-8x8-v0', entry_point='gym_minigrid.envs:Helper8x8Env' )