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.DockerAgent("b3c934102b5b", port=12345),
agents.FrankfurtAgent(),
agents.DockerAgent("b3c934102b5b", port=12346),
agents.FrankfurtAgent(),
# agents.DockerAgent("pommerman/simple-agent", port=12345),
]
# Make the "Free-For-All" environment using the agent list
env = pommerman.make('PommeTeamCompetition-v0', agent_list)
# Run the episodes just like OpenAI Gym
for i_episode in range(1):
state = env.reset(9999)
done = False
while not done:
env.render() #record_json_dir='/tmp/'
actions = env.act(state)
state, reward, done, info = env.step(actions)
#time.sleep(0.1)
print(info)
print('Episode {} finished'.format(i_episode))
env.close()
def main():
"""Simple function to bootstrap a game"""
# Print all possible environments in the Pommerman registry
print(pommerman.REGISTRY)
# Create a set of agents (exactly four)
agent_list = [
agents.SimpleAgent(),
agents.DockerAgent("ccc", port=12345),
agents.SimpleAgent(),
agents.DockerAgent("ccc", port=12345),
]
# Make the "Free-For-All" environment using the agent list
env = pommerman.make('PommeTeamCompetition-v0', agent_list)
#env = pommerman.make(''PommeTeamCompetition-v1'', 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.SimpleAgent(),
#agents.SimpleAgent(),
#agents.SimpleAgent(),
agents.DockerAgent("pommerman/rl_agent", port=12367),
agents.DockerAgent("pommerman/rl_agent", port=12397),
]
# Make the "Free-For-All" environment using the agent list
env = pommerman.make('PommeRadio-v2', 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.RandomAgent(),
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('PommeTeamCompetition-v1', 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))
print("Final Result: ", info)
env.close()
def main():
# 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.RandomAgent(),
agents.DockerAgent("pommerman/agent47agent", port=10080),
]
# 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(agent_names,
competition="comp1",
render=False,
interactive=False,
record_json_dir=None):
# List of four agents
agent_list = [
agents.DockerAgent(competition + "/" + agent_names[i], port=12345 + i)
for i in range(4)
]
# Environment
config = 'PommeFFACompetition-v0'
env = pommerman.make(config, agent_list)
# Run
state = env.reset()
done = False
step = 0
if record_json_dir is not None:
suffix = '%d.json' % step
info = env.get_json_info()
with open(os.path.join(record_json_dir, suffix), 'w') as f:
f.write(json.dumps(info, sort_keys=True, indent=4))
while not done:
step += 1
if render:
env.render()
actions = env.act(state)
state, reward, done, info = env.step(actions)
if record_json_dir is not None:
suffix = '%d.json' % step
info = env.get_json_info()
with open(os.path.join(record_json_dir, suffix), 'w') as f:
f.write(json.dumps(info, sort_keys=True, indent=4))
if interactive:
sys.stdin.readline()
env.close()
return reward
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)
##############
sess = tf.InteractiveSession()
params = joblib.load('parametersepoch8')
# Create a set of agents (exactly four)
agent_list = [
agents.SimpleAgent(),
# agents.DockerAgent("pommerman/brain-agent", port=12345),
agents.SimpleAgent(),
agents.SimpleAgent(),
agents.DockerAgent("pommerman/simple-agent", port=10080),
# convNetwork(sess,params)
# agents.PlayerAgent(),
#TODO 建立一个镜像
]
# Make the "Free-For-All" environment using the agent list
env = pommerman.make('PommeTeamCompetition-v0', agent_list)
# Run the episodes just like OpenAI Gym
for i_episode in range(1):
state = env.reset()
done = False
while not done:
actions = env.act(state)
state, reward, done, info = env.step(actions)
print(done)
print('Episode {} finished'.format(i_episode))
print(reward)
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.DockerAgent("70ab248bd109", port=12345), #Eisenach
agents.DockerAgent("0d29bcb35603", port=12347), #Frankfurt
agents.DockerAgent("70ab248bd109", port=12346),
agents.DockerAgent("0d29bcb35603", port=12348),
# agents.DockerAgent("pommerman/simple-agent", port=12345),
]
# Make the "Free-For-All" environment using the agent list
env = pommerman.make('PommeRadio-v2', agent_list)
wins = 0
ties = 0
survived_agents = []
nof_plays = 1
# Run the episodes just like OpenAI Gym
for i_episode in range(nof_plays):
print("Game " + str(i_episode))
state = env.reset(i_episode)
done = False
while not done:
#env.render()
actions = env.act(state)
state, reward, done, info = env.step(actions)
print(info)
if info['result'] == pommerman.constants.Result.Tie:
ties += 1
elif info['winners'] == [1, 3]:
wins += 1
else:
print(info['result'])
print(info['winners'])
print("Lost with seed: " + str(i_episode))
print('Episode {} finished'.format(i_episode))
survived_agents.extend(state[0]['alive'])
env.close()
survived_team_0 = survived_agents.count(10) + survived_agents.count(12)
survived_team_1 = survived_agents.count(11) + survived_agents.count(13)
kills = nof_plays * 2 - survived_team_0
death = nof_plays * 2 - survived_team_1
print("kills / death / ratio: ", kills, " / ", death, " / ",
kills / max(0.1, death))
winRatio = str(wins / max(1, (nof_plays - ties)))
print("wins: " + str(wins) + "/" + str(nof_plays - ties) + " = " +
winRatio)
file = open("/tmp/hypertune_result_winrate.txt", "w")
file.write(winRatio)
file.close()
file = open("/tmp/hypertune_result_killdeath_diff.txt", "w")
file.write(str(kills - death))
file.close()
