def main():
'''Simple function to bootstrap a game.
Use this as an example to set up your training env.
'''
# Print all possible environments in the Pommerman registry
print(pommerman.REGISTRY)
# Create a set of agents (exactly four)
agent_list = [
# RandAgent(),
# SuicideAgent(),
agents.PlayerAgent(agent_control="wasd"
), # W,A,S,D to move, E to lay bomb
StopAgent(),
StopAgent(),
# StopAgent(),
# agents.SimpleAgent(),
# agents.SimpleAgent(),
# agents.SimpleAgent(),
# agents.SimpleAgent(),
agents.PlayerAgent(agent_control="arrows"
), # arrows to move, space to lay bomb
# SuicideAgent(),
# SuicideAgent(),
# SuicideAgent(),
# agents.DockerAgent("pommerman/simple-agent", port=12345),
]
# Make the "Free-For-All" environment using the agent list
env = pommerman.make('PommeRadioCompetition-v2', agent_list)
# Run the episodes just like OpenAI Gym
for i_episode in range(100):
print('Start to reset')
state = env.reset()
print('Reset complete')
done = False
while not done:
actions = env.act(state)
# print(actions[0])
# actions[0] = [actions[0], 1, 1]
state, reward, done, info = env.step(actions)
bomb_life = state[0]['bomb_life']
bomb_strenth = state[0]['bomb_blast_strength']
bomb_life = feature_utils.get_bomb_life(state[0])
# obs = featurize(state[0], env.position_trav)
env.render()
# print(reward)
# print()
print(info)
print('Episode {} finished'.format(i_episode))
env.close()
def main():
'''Simple function to bootstrap a game.
Use this as an example to set up your training env.
'''
# Print all possible environments in the Pommerman registry
print(pommerman.REGISTRY)
# Create a set of agents (exactly four)
agent_list = [
agents.SimpleAgent(),
agents.SimpleAgent(),
agents.SimpleAgent(),
agents.PlayerAgent(agent_control="arrows"),
# agents.DockerAgent("pommerman/simple-agent", port=12345),
]
# Make the "Free-For-All" environment using the agent list
env = pommerman.make('PommeFFACompetition-v0', agent_list)
# Run the episodes just like OpenAI Gym
for i_episode in range(1):
state = env.reset()
done = False
while not done:
env.render()
actions = env.act(state)
state, reward, done, info = env.step(actions)
print('Episode {} finished'.format(i_episode))
env.close()
def main():
'''Simple function to bootstrap a game.
Use this as an example to set up your training env.
'''
# Print all possible environments in the Pommerman registry
print(pommerman.REGISTRY)
# Create a set of agents (exactly four)
agent_list = [
#agents.SimpleAgent(),
agents.PlayerAgent(
), #note player agent can act as a stand-in for a static agent
agents.RandomAgent(),
#agents.DockerAgent("multiagentlearning/hakozakijunctions", port=12345),
#agents.DockerAgent("multiagentlearning/eisenach", port=12345),
#agents.DockerAgent("multiagentlearning/skynet955", port=12345),
]
# Make the "Free-For-All" environment using the agent list
env = pommerman.make('Simple-v0', agent_list)
# Run the episodes just like OpenAI Gym
for i_episode in range(1):
state = env.reset()
done = False
while not done:
env.render() #comment out to disable slow rendering
actions = env.act(state)
state, reward, done, info = env.step(actions)
print('Episode {} finished'.format(i_episode))
print("Final Result: ", info)
env.close()
def main():
print(pommerman.REGISTRY)
# Create a set of agents (exactly four)
agent_list = [
agents.PlayerAgent(agent_control="arrows"),
agents.SimpleAgent(),
agents.SimpleAgent(),
agents.SimpleAgent()
]
# Play with AI with agent list bellow
# agent_list = [
# agents.SimpleAgent(),
# agents.PlayerAgent(agent_control="arrows"), # Arrows = Move, Space = Bomb
# agents.SimpleAgent(),
# agents.PlayerAgent(agent_control="wasd"), # W,A,S,D = Move, E = Bomb
# ]
# Make the "Free-For-All" environment using the agent list
env = pommerman.make('PommeFFACompetition-v0', agent_list)
# Run the episodes just like OpenAI Gym
for i_episode in range(3):
state = env.reset()
done = False
while not done:
env.render()
actions = env.act(state)
state, reward, done, info = env.step(actions)
print('Episode {} finished'.format(i_episode))
env.close()
def main(unused_args):
'''Simple function to bootstrap a game.
Use this as an example to set up your training env.
'''
# Print all possible environments in the Pommerman registry
print(pommerman.REGISTRY)
# Create a set of agents (exactly four)
agent_list = [
agents.PlayerAgent(agent_control="arrows"),
# custom_agents.ActorCriticAgent(name='smith', update_turn=0, savepath='saved_models', load=FLAGS.load, load_replay=False),
# custom_agents.ActorCriticAgent(name='smith', update_turn=1, savepath='saved_models', load=FLAGS.load),
# custom_agents.ActorCriticAgent(name='smith', update_turn=2, savepath='saved_models', load=FLAGS.load),
# custom_agents.ActorCriticAgent(name='smith', update_turn=3, savepath='saved_models', load=FLAGS.load),
# custom_agents.ActorCriticAgent(name='john', savepath='saved_models', load=FLAGS.load),
# custom_agents.ActorCriticAgent(name='romanov', savepath='saved_models', load=FLAGS.load),
# custom_agents.ActorCriticAgent(name='witch', savepath='saved_models', load=FLAGS.load),
custom_agents.StaticAgent(),
custom_agents.StaticAgent(),
custom_agents.StaticAgent(),
# custom_agents.DebugAgent(),
# agents.SimpleAgent(),
# agents.SimpleAgent(),
# agents.SimpleAgent(),
# agents.RandomAgent(),
# agents.DockerAgent("pommerman/simple-agent", port=12345),
]
# Make the "Free-For-All" environment using the agent list
env = pommerman.make('PommeFFACompetition-v0', agent_list)
# env = pommerman.make('PommeTeamCompetition-v0', agent_list)
# Run the episodes just like OpenAI Gym
for i_episode in range(int(1e6)):
state = env.reset()
done = False
while not done:
if FLAGS.render:
env.render()
actions = env.act(state)
# print(actions)
# quit()
state, reward, done, info = env.step(actions)
# if len(state[0]['alive']) < 4:
# reward = agent_list[0].episode_end(-1)
# break
# input()
# reward = agent_list[0].prev_sub_reward
# avg_reward = agent_list[0].avg_sub_reward
# if i_episode % 5 == 0:
# print('Episode {} finished - Reward: {:.3f} - Avg: {:.3f}'.format(i_episode, reward, avg_reward))
# print(info)
env.close()
def main():
env = pommerman.make('PommeFFACompetition-v0', [
agents.PlayerAgent(),
agents.SimpleAgent(),
agents.RandomAgent(),
StoppingAgent(),
])
for i_episode in range(1):
state = env.reset()
done = False
while not done:
env.render()
actions = env.act(state)
state, reward, done, info = env.step(actions)
if done:
win_player = info['winners'][0] + 1
print(f'win {win_player}P')
print(f'Episode {i_episode} finished')
env.close()
def start_gameplay(self, num_players=4, simulation=True):
# Create a set of agents (exactly four)
agent_list = [agents.SimpleAgent() for i in range(num_players)]
if not simulation:
agent_list[0] = agents.PlayerAgent()
# Make the "Free-For-All" environment using the agent list
env = pommerman.make('PommeFFACompetition-v0', agent_list)
# Run the episodes just like OpenAI Gym
for i_episode in range(1):
state = env.reset()
env.save_json('./replay')
done = False
while not done:
env.render()
actions = env.act(state)
self.game_replay.append(actions)
state, reward, done, info = env.step(actions)
env.close()
self.game_replay.append(num_players)
self.game_over_screen.show()