def run(args):
max_time_steps = 1000
map_size = tuple(args.map_size)
number_of_snakes = args.number_of_snakes
state_shape = (map_size[0], map_size[1], (1 + number_of_snakes))
env = BattlesnakeGym(map_size=map_size, number_of_snakes=number_of_snakes)
agents = []
for i in range(number_of_snakes):
agent = Agent_mxnet(state_shape=state_shape, action_size=4, seed=0)
agent.qnetwork_local.load_parameters(args.model_name.format(i),
ctx=ctx)
state = env.reset()
for t in range(max_time_steps):
actions = []
for i in range(number_of_snakes):
state_agent_i = sort_states_for_snake_id(state, i + 1)
action = agent.act(state_agent_i, eps=0)
actions.append(action)
next_state, reward, done, _ = env.step(np.array(actions))
env.render(mode=args.render_mode)
time.sleep(0.2)
state = next_state
if done:
break
class MultiAgentBattlesnake(MultiAgentEnv):
MAX_MAP_HEIGHT = 21
def __init__(self, num_agents, map_height, heuristics, rewards=SimpleRewards()):
observation_type = "max-bordered-51s"
self.env = BattlesnakeGym(
observation_type=observation_type,
number_of_snakes=num_agents,
map_size=(map_height, map_height), rewards=rewards)
self.observation_height = self.MAX_MAP_HEIGHT
self.action_space = self.env.action_space[0]
gym_observation_space = gym.spaces.Box(low=-1.0, high=5.0,
shape=(self.observation_height, self.observation_height, 6), dtype=np.float32)
self.observation_space = gym.spaces.Dict({
"action_mask": gym.spaces.Box(0, 1, shape=(4,),
dtype=np.float32),
"state": gym_observation_space})
self.num_agents = num_agents
self.observation_type = observation_type
self.old_obs1 = {}
self.heuristics = heuristics
if len(self.heuristics) > 0:
self.battlesnake_heuristics = MyBattlesnakeHeuristics()
self.heuristics_list = []
for heuristic_name in self.heuristics:
if heuristic_name == "banned_forbidden_moves":
self.heuristics_list.append(self.battlesnake_heuristics.banned_forbidden_moves)
elif heuristic_name == "banned_wall_hits":
self.heuristics_list.append(self.battlesnake_heuristics.banned_wall_hits)
self.rewards = rewards
def set_effective_map_size(self, eff_map_size):
self.__init__(self.num_agents, eff_map_size, self.heuristics, self.rewards)
self.reset()
def reset(self):
self.mask = {}
new_obs, _, _, info = self.env.reset()
obs = {}
# add empty map placeholders for use until we've seen 2 steps
empty_map = np.zeros((self.observation_height, self.observation_height, 3))
new_obs = np.array(new_obs, dtype=np.float32)
for i in range(self.num_agents):
agent_id = "agent_{}".format(i)
obs_i = sort_states_for_snake_id(new_obs, i+1)
merged_map = np.concatenate((empty_map, obs_i), axis=-1)
if len(self.heuristics) > 0:
health = {k: 100 for k in range(self.num_agents)}
mask = self.battlesnake_heuristics.get_action_masks_from_functions(
obs_i, i, 0, health, self.env,
functions=self.heuristics_list)
else:
mask = np.array([1, 1, 1, 1])
obs[agent_id] = {"state": merged_map, "action_mask": mask}
self.mask[agent_id] = obs[agent_id]["action_mask"]
self.old_obs1[agent_id] = obs_i
return obs
def step(self, action_dict):
actions = []
for key, value in sorted(action_dict.items()):
actions.append(value)
o, r, d, info = self.env.step(actions)
rewards = {}
obs = {}
infos = {}
for i, key in enumerate(sorted(action_dict.keys())):
old_obs1 = self.old_obs1[key]
obs_i = np.array(o, dtype=np.float32)
obs_i = sort_states_for_snake_id(obs_i, i+1)
merged_map = np.concatenate((old_obs1, obs_i), axis=-1)
infos[key] = info
rewards[key] = r[i]
if len(self.heuristics) > 0 and self.env.snakes.get_snakes()[i].is_alive():
turn_count = info["current_turn"]+1
health = info["snake_health"]
mask = self.battlesnake_heuristics.get_action_masks_from_functions(
obs_i, i, turn_count, health, self.env,
functions=self.heuristics_list)
else:
mask = np.array([1, 1, 1, 1])
obs[key] = {"state": merged_map, "action_mask": mask}
self.old_obs1[key] = np.array(obs_i, dtype=np.float32)
self.mask[key] = obs[key]["action_mask"]
dead_count = 0
for x in range(self.num_agents):
if d[x] == True:
dead_count += 1
dones = {'__all__': dead_count >= self.num_agents-1}
return obs, rewards, dones, infos
class MultiAgentBattlesnake(MultiAgentEnv):
MAX_MAP_HEIGHT = 21
def __init__(self, num_agents, map_height):
observation_type = "max-bordered-51s"
self.env = BattlesnakeGym(
observation_type=observation_type,
number_of_snakes=num_agents,
map_size=(map_height, map_height))
if "bordered" in observation_type:
if "max-bordered" in observation_type:
self.observation_height = self.MAX_MAP_HEIGHT
else: # If only bordered with 2 rows of -1
self.observation_height = map_height + 2
else: # Flat without border
self.observation_height = map_height
self.action_space = self.env.action_space[0]
self.observation_space = gym.spaces.Box(low=-1.0, high=5.0, shape=(self.observation_height, self.observation_height, 6), dtype=np.float32)
self.num_agents = num_agents
self.observation_type = observation_type
self.old_obs1 = {}
def set_effective_map_size(self, eff_map_size):
self.__init__(self.num_agents, eff_map_size)
self.reset()
def reset(self):
new_obs, _, _, info = self.env.reset()
obs = {}
# add empty map placeholders for use until we've seen 2 steps
empty_map = np.zeros((self.observation_height, self.observation_height, 3))
new_obs = np.array(new_obs, dtype=np.float32)
for i in range(self.num_agents):
agent_id = "agent_{}".format(i)
obs_i = sort_states_for_snake_id(new_obs, i+1)
merged_map = np.concatenate((empty_map, obs_i), axis=-1)
if self.num_agents > 1:
obs[agent_id] = merged_map
else:
obs[agent_id] = merged_map
self.old_obs1[agent_id] = obs_i
return obs
def render(self):
self.env.render()
def step(self, action_dict):
actions = []
for key, value in sorted(action_dict.items()):
actions.append(value)
o, r, d, info = self.env.step(actions)
rewards = {}
obs = {}
infos = {}
for i, key in enumerate(sorted(action_dict.keys())):
old_obs1 = self.old_obs1[key]
o_i = np.array(o, dtype=np.float32)
o_i = sort_states_for_snake_id(o_i, i+1)
merged_map = np.concatenate((old_obs1, o_i), axis=-1)
infos[key] = info
if self.num_agents > 1:
rewards[key] = r[i]
obs[key] = merged_map
else:
rewards[key] = r
obs[key] = merged_map
self.old_obs1[key] = np.array(o_i, dtype=np.float32)
dead_count = 0
for x in range(self.num_agents):
if d[x] == True:
dead_count += 1
dones = {'__all__': dead_count >= self.num_agents-1}
return obs, rewards, dones, infos