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
# 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)
# Seed and reset the environment
env.seed(0)
obs = env.reset()
# Run the agents until we're done
done = False
while not done:
env.render()
actions = env.act(obs) # brauch ich nicht
#actions = [action % 4 for action in actions]
#actions = [0,actions[1]]
obs, reward, done, info = env.step(actions)
#kacka = featurize(obs[0])
env.render(close=True)
env.close()
# Print the result
class MultiAgent(MultiAgentEnv):
def __init__(self):
super(MultiAgent, self).__init__()
self.phase = 0
self.setup()
def setup(self):
agents = []
if self.phase == 0:
self.agents_index = [1, 3]
self.enemies_agents_index = [0, 2]
config = team_v0_fast_env()
config["env_kwargs"]["num_wood"] = 2
config["env_kwargs"]["num_items"] = 2
config["env_kwargs"]["num_rigid"] = 20
agents.insert(
0, SuicidalAgent(config["agent"](0, config["game_type"])))
agents.insert(2, NoDoAgent(config["agent"](2,
config["game_type"])))
print(config["env_kwargs"])
self.env = Pomme(**config["env_kwargs"])
self.env.seed()
if self.phase == 1:
self.agents_index = [1, 3]
self.enemies_agents_index = [0, 2]
config = team_v0_fast_env()
config["env_kwargs"]["num_wood"] = 2
config["env_kwargs"]["num_items"] = 2
config["env_kwargs"]["num_rigid"] = 36
agents.insert(
0, SuicidalAgent(config["agent"](0, config["game_type"])))
agents.insert(2, NoDoAgent(config["agent"](2,
config["game_type"])))
print(config["env_kwargs"])
self.env = Pomme(**config["env_kwargs"])
self.env.seed()
if self.phase == 2:
self.agents_index = [1, 3]
self.enemies_agents_index = [0, 2]
config = team_v0_fast_env()
config["env_kwargs"]["num_wood"] = 2
config["env_kwargs"]["num_items"] = 2
config["env_kwargs"]["num_rigid"] = 36
agents.insert(0, NoDoAgent(config["agent"](0,
config["game_type"])))
agents.insert(2, NoDoAgent(config["agent"](2,
config["game_type"])))
print(config["env_kwargs"])
self.env = Pomme(**config["env_kwargs"])
self.env.seed()
if self.phase == 3:
self.agents_index = [1, 3]
self.enemies_agents_index = [0, 2]
config = team_v0_fast_env()
config["env_kwargs"]["num_wood"] = 2
config["env_kwargs"]["num_items"] = 2
config["env_kwargs"]["num_rigid"] = 36
agents.insert(0, NoDoAgent(config["agent"](0,
config["game_type"])))
agents.insert(2, NoDoAgent(config["agent"](2,
config["game_type"])))
print(config["env_kwargs"])
self.env = Pomme(**config["env_kwargs"])
self.env.seed()
if self.phase == 4:
self.agents_index = [1, 3]
self.enemies_agents_index = [0, 2]
config = team_v0_fast_env()
config["env_kwargs"]["num_wood"] = 0
config["env_kwargs"]["num_items"] = 10
config["env_kwargs"]["num_rigid"] = 36
agents.insert(
0, SuicidalAgent(config["agent"](0, config["game_type"])))
agents.insert(2,
SimpleAgent(config["agent"](2, config["game_type"])))
print(config["env_kwargs"])
self.env = Pomme(**config["env_kwargs"])
self.env.seed()
for agent_id in self.agents_index:
agents.insert(
agent_id,
BaseLineAgent(config["agent"](agent_id, config["game_type"])))
self.env.set_agents(agents)
self.env.set_init_game_state(None)
self.observation_space = spaces.Dict({
"boards":
spaces.Box(low=-1, high=20, shape=(3, 11, 11)),
"states":
spaces.Box(low=-1, high=20, shape=(9, )),
})
spaces.Box(low=-1.0, high=20.0, shape=(372, ), dtype=np.float32)
self.action_space = self.env.action_space
def set_phase(self, phase):
print("learn phase " + str(phase))
self.phase = phase
self.setup()
self.reset()
def step(self, actions):
obs = self.env.get_observations()
all_actions = self.env.act(obs)
for index in self.agents_index:
try:
action = actions[index]
except:
action = 0
all_actions[index] = action
step_obs = self.env.step(all_actions)
obs, rew, done, info = {}, {}, {}, {}
for i in actions.keys():
obs[i], rew[i], done[i], info[i] = [
featurize(step_obs[0][i]),
step_obs[1][i],
step_obs[1][i] == -1 or step_obs[2],
step_obs[3],
]
done["__all__"] = step_obs[2]
return obs, rew, done, info
def reset(self):
obs = self.env.reset()
return {i: featurize(obs[i]) for i in self.agents_index}
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
def main():
# Print all possible environments in the Pommerman registry
# Instantiate the environment
DETERMINISTIC = False
VISUALIZE = False
if args.test:
DETERMINISTIC = True
VISUALIZE = True
config = ffa_competition_env()
env = Pomme(**config["env_kwargs"])
env.seed(0)
# Create a Proximal Policy Optimization agent
with open('ppo.json', 'r') as fp:
agent = json.load(fp=fp)
with open('mlp2_lstm_network.json', 'r') as fp:
network = json.load(fp=fp)
agent = Agent.from_spec(
spec=agent,
kwargs=dict(
states=dict(type='float', shape=env.observation_space.shape),
actions=dict(type='int', num_actions=env.action_space.n),
network=network
)
)
# Add 3 random agents
agents = []
for agent_id in range(3):
agents.append(SimpleAgent(config["agent"](agent_id, config["game_type"])))
# Add TensorforceAgent
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)
# Instantiate and run the environment for 5 episodes.
if VISUALIZE:
wrapped_env = WrappedEnv(env, True)
else:
wrapped_env = WrappedEnv(env)
runner = Runner(agent=agent, environment=wrapped_env)
rewards = []
episodes = []
def episode_finished(r):
nonlocal episodes
nonlocal rewards
print("Finished episode {ep} after {ts} timesteps (reward: {reward})".format(ep=r.episode, ts=r.episode_timestep,
reward=r.episode_rewards[-1]))
if r.episode % 1000 == 0:
agent.save_model(('./{}').format(EXPERIMENT_NAME), False)
try:
prev_data = pickle.load(open(EXPERIMENT_NAME, "rb"))
prev_len = len(prev_data[0])
prev_data[0].extend(rewards)
rewards = []
prev_data[1].extend(episodes)
episodes = []
pickle.dump(prev_data, open(EXPERIMENT_NAME, "wb"))
except (OSError, IOError) as e:
pickle.dump([rewards, episodes], open(EXPERIMENT_NAME, "wb"))
if r.episode_rewards[-1] >= 5:
print()
print()
print()
print("WINNER WINNER CHICKEN DINNER")
episodes.append(r.episode)
rewards.append(r.episode_rewards[-1])
return True
# Restore, Train, and Save Model
if args.test or args.resume: # If test, change settings and restore model
agent.restore_model('./','PPO_K_someS_500batch_biggerreward_99dis')
runner.run(episodes=EPISODES, max_episode_timesteps=2000, episode_finished=episode_finished, deterministic=False)
if not args.test:
agent.save_model(('./{}').format(EXPERIMENT_NAME), False)
print("Stats: ", runner.episode_rewards[-5:], runner.episode_timesteps[-5:])
#Dump reward values
try:
prev_data = pickle.load(open(EXPERIMENT_NAME, "rb"))
prev_len = len(prev_data[0])
prev_data[0].extend(rewards)
prev_data[1].extend(episodes)
print(episodes)
pickle.dump(prev_data, open(EXPERIMENT_NAME, "wb"))
except (OSError, IOError) as e:
pickle.dump([rewards, episodes], open(EXPERIMENT_NAME, "wb"))
try:
runner.close()
except AttributeError as e:
pass
