from gym_minigrid.minigrid import * from gym_minigrid.register import register from operator import add class DynamicObstaclesEnv(MiniGridEnv): """ Single-room square grid environment with moving obstacles """ def __init__( self, size=8, agent_start_pos=(1, 1), agent_start_dir=0, n_obstacles=4 ): self.agent_start_pos = agent_start_pos self.agent_start_dir = agent_start_dir # Reduce obstacles if there are too many if n_obstacles <= size/2 + 1: self.n_obstacles = int(n_obstacles) else: self.n_obstacles = int(size/2) super().__init__( grid_size=size, max_steps=4 * size * size, # Set this to True for maximum speed see_through_walls=True, ) # Allow only 3 actions permitted: left, right, forward self.action_space = spaces.Discrete(self.actions.forward + 1) self.reward_range = (-1, 1) def _gen_grid(self, width, height): # Create an empty grid self.grid = Grid(width, height) # Generate the surrounding walls self.grid.wall_rect(0, 0, width, height) # Place a goal square in the bottom-right corner self.grid.set(width - 2, height - 2, Goal()) # Place the agent if self.agent_start_pos is not None: self.agent_pos = self.agent_start_pos self.agent_dir = self.agent_start_dir else: self.place_agent() # Place obstacles self.obstacles = [] for i_obst in range(self.n_obstacles): self.obstacles.append(Ball()) self.place_obj(self.obstacles[i_obst], max_tries=100) self.mission = "get to the green goal square" def step(self, action): # Invalid action if action >= self.action_space.n: action = 0 # Check if there is an obstacle in front of the agent front_cell = self.grid.get(*self.front_pos) not_clear = front_cell and front_cell.type != 'goal' # Update obstacle positions for i_obst in range(len(self.obstacles)): old_pos = self.obstacles[i_obst].cur_pos top = tuple(map(add, old_pos, (-1, -1))) try: self.place_obj(self.obstacles[i_obst], top=top, size=(3,3), max_tries=100) self.grid.set(*old_pos, None) except: pass # Update the agent's position/direction obs, reward, done, info = MiniGridEnv.step(self, action) # If the agent tried to walk over an obstacle or wall if action == self.actions.forward and not_clear: reward = -1 done = True return obs, reward, done, info return obs, reward, done, info class DynamicObstaclesEnv5x5(DynamicObstaclesEnv): def __init__(self): super().__init__(size=5, n_obstacles=2) class DynamicObstaclesRandomEnv5x5(DynamicObstaclesEnv): def __init__(self): super().__init__(size=5, agent_start_pos=None, n_obstacles=2) class DynamicObstaclesEnv6x6(DynamicObstaclesEnv): def __init__(self): super().__init__(size=6, n_obstacles=3) class DynamicObstaclesRandomEnv6x6(DynamicObstaclesEnv): def __init__(self): super().__init__(size=6, agent_start_pos=None, n_obstacles=3) class DynamicObstaclesEnv16x16(DynamicObstaclesEnv): def __init__(self): super().__init__(size=16, n_obstacles=8) register( id='MiniGrid-Dynamic-Obstacles-5x5-v0', entry_point='gym_minigrid.envs:DynamicObstaclesEnv5x5' ) register( id='MiniGrid-Dynamic-Obstacles-Random-5x5-v0', entry_point='gym_minigrid.envs:DynamicObstaclesRandomEnv5x5' ) register( id='MiniGrid-Dynamic-Obstacles-6x6-v0', entry_point='gym_minigrid.envs:DynamicObstaclesEnv6x6' ) register( id='MiniGrid-Dynamic-Obstacles-Random-6x6-v0', entry_point='gym_minigrid.envs:DynamicObstaclesRandomEnv6x6' ) register( id='MiniGrid-Dynamic-Obstacles-8x8-v0', entry_point='gym_minigrid.envs:DynamicObstaclesEnv' ) register( id='MiniGrid-Dynamic-Obstacles-16x16-v0', entry_point='gym_minigrid.envs:DynamicObstaclesEnv16x16' )