def agent_factory(name, role, kind, clients, max_episodes, max_actions, logdir,
quit):
assert len(
clients
) >= 2, 'There are not enough Malmo clients in the pool (need at least 2)'
clients = parse_clients_args(clients)
visualizer = ConsoleVisualizer(prefix='Agent %d' % role)
if role == 0:
env = PigChaseEnvironment(clients,
PigChaseSymbolicStateBuilder(),
actions=ENV_ACTIONS,
role=role,
human_speed=True,
randomize_positions=True)
agent = PigChaseChallengeAgent(name)
if type(agent.current_agent) == RandomAgent:
agent_type = PigChaseEnvironment.AGENT_TYPE_1
else:
agent_type = PigChaseEnvironment.AGENT_TYPE_2
obs = env.reset(agent_type)
reward = 0
rewards = []
done = False
episode = 0
while True:
# select an action
action = agent.act(obs, reward, done, True)
if done:
visualizer << (episode + 1, 'Reward', sum(rewards))
rewards = []
episode += 1
if type(agent.current_agent) == RandomAgent:
agent_type = PigChaseEnvironment.AGENT_TYPE_1
else:
agent_type = PigChaseEnvironment.AGENT_TYPE_2
obs = env.reset(agent_type)
# take a step
obs, reward, done = env.do(action)
rewards.append(reward)
else:
env = PigChaseEnvironment(clients,
PigChaseSymbolicStateBuilder(),
actions=list(ARROW_KEYS_MAPPING.values()),
role=role,
randomize_positions=True)
env.reset(PigChaseEnvironment.AGENT_TYPE_3)
agent = PigChaseHumanAgent(name, env, list(ARROW_KEYS_MAPPING.keys()),
max_episodes, max_actions, visualizer, quit)
agent.show()
def run_maze_learner(mission, clients):
if 'malmopy.visualization.tensorboard' in sys.modules:
visualizer = TensorboardVisualizer()
visualizer.initialize(logdir, None)
else:
visualizer = ConsoleVisualizer()
# with TensorboardVisualizer() as visualizer:
env = MazeEnvironment(mission,
[str.split(client, ':') for client in clients])
env.recording = False
# explorer = LinearEpsilonGreedyExplorer(1, 0.1, 10000)
# model = DeepQNeuralNetwork((4, 84, 84), (env.available_actions,), momentum=0, visualizer=visualizer)
# memory = TemporalMemory(50000, model.input_shape[1:], model.input_shape[0], False)
agent = RandomAgent(
"rand", 3
) #DQNAgent("Maze DQN Agent", env.available_actions, model, memory, explorer=explorer,
#visualizer=visualizer)
# exp = SingleAgentExperiment("Malmo Cliff Walking", agent, env, 500000, warm_up_timesteps=500,
# visualizer=visualizer)
# exp.episode_end += on_episode_end
# visualizer.initialize(MALMO_MAZE_FOLDER, model, CntkConverter())
# with Popen(['tensorboard', '--logdir=%s' % path.join(MALMO_MAZE_FOLDER, path.pardir), '--port=6006']):
EPOCH_SIZE = 250000
max_training_steps = 50 * EPOCH_SIZE
state = env.reset()
reward = 0
agent_done = False
viz_rewards = []
for step in range(1, max_training_steps + 1):
# action = agent.act(state, reward, agent_done, is_training=True)
# check if env needs reset
if env.done:
visualize_training(visualizer, step, viz_rewards)
agent.inject_summaries(step)
viz_rewards = []
state = env.reset()
# select an action
action = agent.act(state, reward, agent_done, is_training=True)
print('ACTION BEING TAKEN: ', action)
# take a step
state, reward, agent_done = env.do(action)
viz_rewards.append(reward)
if (step % EPOCH_SIZE) == 0:
model.save('%s-%s-dqn_%d.model' %
(backend, environment, step / EPOCH_SIZE))
def run_maze_learner(mission, clients):
if 'malmopy.visualization.tensorboard' in sys.modules:
visualizer = TensorboardVisualizer()
visualizer.initialize(logdir, None)
else:
visualizer = ConsoleVisualizer()
env = MazeEnvironment(mission,
[str.split(client, ':') for client in clients])
env.recording = False
agent = TabularQLearnerAgent("rand", 3)
#taking random actions
EPOCH_SIZE = 250000
max_training_steps = 50 * EPOCH_SIZE
state = env.reset()
reward = 0
agent_done = False
viz_rewards = []
for step in range(1, max_training_steps + 1):
# check if env needs reset
if env.done:
visualize_training(visualizer, step, viz_rewards)
agent.inject_summaries(step)
viz_rewards = []
state = env.reset()
# select an action
action = agent.act(step, state, is_training=True)
if type(action) == int:
print('ACTION BEING TAKEN: ', action)
else:
print('ACTION BEING TAKEN: ', np.asscalar(action))
# take a step
old = state
state, reward, agent_done = env.do(action)
agent.observe(old, action, state, reward, env.done)
viz_rewards.append(reward)
if (step % EPOCH_SIZE) == 0:
model.save('%s-%s-dqn_%d.model' %
(backend, environment, step / EPOCH_SIZE))
help='The type of baseline to run.')
arg_parser.add_argument('-e',
'--epochs',
type=int,
default=5,
help='Number of epochs to run.')
arg_parser.add_argument('clients',
nargs='*',
default=['127.0.0.1:10000', '127.0.0.1:10001'],
help='Minecraft clients endpoints (ip(:port)?)+')
args = arg_parser.parse_args()
logdir = BASELINES_FOLDER % ('bayes_agent', datetime.utcnow().isoformat())
if 'malmopy.visualization.tensorboard' in sys.modules:
visualizer = TensorboardVisualizer()
visualizer.initialize(logdir, None)
else:
visualizer = ConsoleVisualizer()
agents = [{
'name': agent,
'role': role,
'type': args.type,
'clients': args.clients,
'max_epochs': args.epochs,
'logdir': logdir,
'visualizer': visualizer
} for role, agent in enumerate(ENV_AGENT_NAMES)]
run_experiment(agents)
def restartGame():
obs = env.reset()
reward = torch.zeros(config.general.batch_size).type_as(dtype)
done = torch.zeros(config.general.batch_size).type_as(dtype)
for agent in agents:
agent.reset()
return obs, reward, done
obs, reward, done = restartGame()
if config.envs.visualize:
visualizer = ConsoleVisualizer(prefix='Agent %d' % 0)
ag2 = PigChaseHumanAgent("Agent_2", env,
list(ARROW_KEYS_MAPPING.keys()),
10, 25, visualizer, quit)
ag2.show()
crt_agent = 0
it = [0, 0]
episode = 0
# Action batch
rew_1 = 0
done_1 = 0
all_rewards = 0
while episode < EVAL_EPISODES:
