def env_for_players(self):
config = ffa_v0_fast_env(30)
env = Pomme(**config["env_kwargs"])
agents = [DQN(config["agent"](0, config["game_type"])),
PlayerAgent(config["agent"](1, config["game_type"])),
RandomAgent(config["agent"](2, config["game_type"])),
RandomAgent(config["agent"](3, config["game_type"]))]
env.set_agents(agents)
env.set_training_agent(agents[0].agent_id) # training_agent is only dqn agent
env.set_init_game_state(None)
return env
def set_pommerman_env(agent_id=0):
# Instantiate the environment
config = ffa_v0_fast_env()
env = Pomme(**config["env_kwargs"])
np.random.seed(0)
env.seed(0)
# Add 3 Simple Agents and 1 DQN agent
agents = [
DQN(config["agent"](agent_id, config["game_type"])) if i == agent_id
else SimpleAgent(config["agent"](i, config["game_type"]))
for i in range(4)
]
env.set_agents(agents)
env.set_training_agent(
agents[agent_id].agent_id) # training_agent is only dqn agent
env.set_init_game_state(None)
return env
def get_env():
config = ffa_v0_fast_env()
env = Pomme(**config["env_kwargs"])
agent_id = 0
agents = [
DQN(config["agent"](0, config["game_type"])),
SimpleAgent(config["agent"](1, config["game_type"])),
SimpleAgent(config["agent"](2, config["game_type"])),
SimpleAgent(config["agent"](3, config["game_type"])),
]
env.set_agents(agents)
env.set_training_agent(agents[agent_id].agent_id)
env.set_init_game_state(None)
return env
if teammate is not None:
teammate = teammate.value
else:
teammate = -1
teammate = make_np_float([teammate])
enemies = obs["enemies"]
enemies = [e.value for e in enemies]
if len(enemies) < 3:
enemies = enemies + [-1]*(3 - len(enemies))
enemies = make_np_float(enemies)
return np.concatenate((board, bomb_blast_strength, bomb_life, position, ammo, blast_strength, can_kick, teammate, enemies))
# Instantiate the environment
config = ffa_v0_fast_env()
env = Pomme(**config["env_kwargs"])
env.action_space.n
# Add 3 random agents
agents = {}
for agent_id in range(3):
agents[agent_id] = StaticAgent(config["agent"](agent_id, config["game_type"]))
# Add human agent
agent_id += 1
agents[3] = PlayerAgent(config["agent"](agent_id, config["game_type"]), "arrows")
env.set_agents(list(agents.values()))
env.set_init_game_state(None)
def setup(self):
agents = []
if self.phase == 0:
arr = [0, 1]
random.shuffle(arr)
agents_index = arr.pop()
op_index = arr.pop()
self.agents_index = [agents_index]
self.enemies_agents_index = [op_index]
self.max_steps = 200
config = ffa_v0_fast_env()
config["env_kwargs"]["max_steps"] = self.max_steps
agents.insert(
agents_index,
BaseLineAgent(config["agent"](agents_index,
config["game_type"])))
agents.insert(
op_index,
NoDoAgent(config["agent"](op_index, config["game_type"])))
self.env = Pomme(**config["env_kwargs"])
self.env.set_agents(agents)
init_state = {
'board_size': '11',
'step_count': '0',
'board': '',
'agents':
'[{"agent_id": 0, "is_alive": true, "position": [1, 1], "ammo": 1, "blast_strength": 2, "can_kick": false}, {"agent_id": 1, "is_alive": true, "position": [9, 0], "ammo": 1, "blast_strength": 2, "can_kick": false}]',
'bombs': '[]',
'flames': '[]',
'items': '[]',
'intended_actions': '[0, 0]'
}
board = np.full((11, 11), 0)
init_state['board'] = json.dumps(board.tolist())
agents_json = json.loads(copy.copy(init_state['agents']))
random_pos = np.random.choice(board.shape[0], (2, 2),
replace=False)
agents_json[0]["position"] = random_pos[0].tolist()
agents_json[1]["position"] = random_pos[1].tolist()
init_state['agents'] = json.dumps(agents_json)
self.env._init_game_state = init_state
self.env.reset()
if self.phase == 1:
arr = [0, 1]
random.shuffle(arr)
agents_index = arr.pop()
op_index = arr.pop()
self.agents_index = [agents_index]
self.enemies_agents_index = [op_index]
self.max_steps = 200
config = ffa_v0_fast_env()
config["env_kwargs"]["max_steps"] = self.max_steps
agents.insert(
agents_index,
BaseLineAgent(config["agent"](agents_index,
config["game_type"])))
agents.insert(
op_index,
NoDoAgent(config["agent"](op_index, config["game_type"])))
self.env = Pomme(**config["env_kwargs"])
self.env.set_agents(agents)
init_state = {
'board_size': '11',
'step_count': '0',
'board': '',
'agents':
'[{"agent_id": 0, "is_alive": true, "position": [1, 1], "ammo": 1, "blast_strength": 2, "can_kick": false}, {"agent_id": 1, "is_alive": true, "position": [9, 0], "ammo": 1, "blast_strength": 2, "can_kick": false}]',
'bombs': '[]',
'flames': '[]',
'items': '[]',
'intended_actions': '[0, 0]'
}
board = np.full((11, 11), 0)
board[5, :] = (np.ones(11) * 2)
agents_json = json.loads(copy.copy(init_state['agents']))
agents_json[0]["position"] = [
random.randint(0, 4),
random.randint(0, 10)
]
agents_json[1]["position"] = [
random.randint(6, 10),
random.randint(0, 10)
]
init_state['agents'] = json.dumps(agents_json)
init_state['board'] = json.dumps(board.tolist())
self.env._init_game_state = init_state
self.env.reset()
self.observation_space = spaces.Dict({
'boards':
spaces.Box(low=-1, high=25, shape=(11, 11, 18), dtype=np.float32),
'states':
spaces.Box(low=-1, high=25, shape=(8, ), dtype=np.float32)
})
self.action_space = self.env.action_space
def main(args):
version = 'v1'
episodes = args.episodes
visualize = args.visualize
config = ffa_v0_fast_env()
env = Pomme(**config["env_kwargs"])
env.seed(0)
agent = PPOAgent(
states=dict(type='float', shape=(11, 11, 12)),
actions=dict(type='int', num_actions=env.action_space.n),
network=[
# (9, 9, 12)
dict(type='conv2d', size=12, window=3, stride=1),
# (7, 7, 8)
dict(type='conv2d', size=8, window=3, stride=1),
# (5, 5, 4)
dict(type='conv2d', size=4, window=3, stride=1),
# (100)
dict(type='flatten'),
dict(type='dense', size=64, activation='relu'),
dict(type='dense', size=16, activation='relu'),
],
batching_capacity=1000,
step_optimizer=dict(type='adam', learning_rate=1e-4))
if os.path.exists(os.path.join('models', version, 'checkpoint')):
agent.restore_model(directory=os.path.join('models', version))
agents = []
for agent_id in range(3):
# agents.append(RandomAgent(config["agent"](agent_id, config["game_type"])))
# agents.append(StoppingAgent(config["agent"](agent_id, config["game_type"])))
agents.append(
SimpleAgent(config["agent"](agent_id, config["game_type"])))
agent_id += 1
agents.append(
TensorforceAgent(config["agent"](agent_id, config["game_type"])))
env.set_agents(agents)
env.set_training_agent(agents[-1].agent_id)
env.set_init_game_state(None)
wrapped_env = WrappedEnv(env, agent, visualize)
runner = Runner(agent=agent, environment=wrapped_env)
try:
runner.run(episodes=episodes, max_episode_timesteps=100)
except Exception as e:
raise e
finally:
agent.save_model(directory=os.path.join('models', version, 'agent'))
win_count = len(
list(filter(lambda reward: reward == 1, runner.episode_rewards)))
print('Stats: ')
print(f' runner.episode_rewards = {runner.episode_rewards}')
print(f' win count = {win_count}')
try:
runner.close()
except AttributeError as e:
raise e
from ray.rllib.agents.ppo import PPOTrainer
from rllib_training.envs import PommeRllib
from pommerman import configs
config = configs.ffa_v0_fast_env()
trainer = PPOTrainer(env=PommeRllib,
config={
"num_workers": 9,
"num_gpus": 1,
"env_config": config,
})
