def agent_factory(name, role, baseline_agent, clients, max_epochs, logdir,
visualizer):
assert len(clients) >= 2, 'Not enough clients (need at least 2)'
clients = parse_clients_args(clients)
builder = PigChaseSymbolicStateBuilder()
env = PigChaseEnvironment(clients,
builder,
role=role,
randomize_positions=True)
if role == 0:
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
agent_done = False
while True:
# select an action
action = agent.act(obs, reward, agent_done, is_training=True)
# reset if needed
if env.done:
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, agent_done = env.do(action)
else:
if baseline_agent == 'astar':
agent = FocusedAgent(name, ENV_TARGET_NAMES[0])
else:
agent = RandomAgent(name, env.available_actions)
obs = env.reset()
reward = 0
agent_done = False
viz_rewards = []
max_training_steps = EPOCH_SIZE * max_epochs
for step in six.moves.range(1, max_training_steps + 1):
# check if env needs reset
if env.done:
visualize_training(visualizer, step, viz_rewards)
viz_rewards = []
obs = env.reset()
# select an action
action = agent.act(obs, reward, agent_done, is_training=True)
# take a step
obs, reward, agent_done = env.do(action)
viz_rewards.append(reward)
agent.inject_summaries(step)
def agent_factory(name,
role,
clients,
max_epochs,
logdir,
visualizer,
manual=False):
assert len(clients) >= 2, 'Not enough clients (need at least 2)'
clients = parse_clients_args(clients)
builder = PigChaseSymbolicStateBuilder()
env = PigChaseEnvironment(clients,
builder,
actions=DanishPuppet.ACTIONS.all_commands(),
role=role,
human_speed=HUMAN_SPEED,
randomize_positions=True)
# Default agent (challenger)
c_agent = ChallengerFactory(name,
focused=True,
random=True,
bad_guy=False,
standstill=False)
# Challenger (Agent_1)
if role == 0:
agent_type = get_agent_type(c_agent.current_agent)
state = env.reset(agent_type)
print("Agent Factory: Assigning {}.".format(
type(c_agent.current_agent).__name__))
reward = 0
agent_done = False
while True:
# select an action
action = c_agent.act(state, reward, agent_done, is_training=True)
# reset if needed
if env.done:
agent_type = get_agent_type(c_agent.current_agent)
_ = env.reset(agent_type)
print("Agent Factory: Assigning {}.".format(
type(c_agent.current_agent).__name__))
# take a step
state, reward, agent_done = env.do(action)
# Our Agent (Agent_2)
else:
c_agent = Jumper(name=name, helmets=c_agent.get_helmets())
# Manual overwrite!
if manual:
c_agent.manual = True
state = env.reset()
reward = 0
agent_done = False
viz_rewards = []
max_training_steps = EPOCH_SIZE * max_epochs
for step in range(1, max_training_steps + 1):
# check if env needs reset
if env.done:
try:
c_agent.note_game_end(reward_sequence=viz_rewards,
state=state[0])
except TypeError:
c_agent.note_game_end(reward_sequence=viz_rewards,
state=None)
print("")
visualize_training(visualizer, step, viz_rewards)
viz_rewards = []
state = env.reset()
# select an action
action = None
frame = None if not PASS_FRAME else env.frame
while action is None:
# for key, item in env.world_observations.items():
# print(key, ":", item)
total_time = None
if env is not None and env.world_observations is not None:
total_time = env.world_observations["TotalTime"]
action = c_agent.act(state,
reward,
done=agent_done,
total_time=total_time,
is_training=True,
frame=frame)
# 'wait'
if action == DanishPuppet.ACTIONS.wait:
action = None
sleep(4e-3)
state = env.state
# take a step
state, reward, agent_done = env.do(action)
viz_rewards.append(reward)
c_agent.inject_summaries(step)
def agent_factory(name, role, baseline_agent, clients, max_epochs, logdir,
visualizer):
assert len(clients) >= 2, 'Not enough clients (need at least 2)'
clients = parse_clients_args(clients)
batch_size = 32
builder = PigChaseSymbolicStateBuilder()
env = PigChaseEnvironment(clients,
builder,
role=role,
randomize_positions=True)
if role == 0:
agent = PigChaseChallengeAgent(name)
if type(agent.current_agent) == RandomAgent:
agent_type = PigChaseEnvironment.AGENT_TYPE_1
else:
agent_type = PigChaseEnvironment.AGENT_TYPE_2
##Aqui el state hay que modificarlo para que se adapte a lo que la red neurnal necesita
state = env.reset(agent_type)
reward = 0
agent_done = False
num_actions = 0
while True:
# take a step
# reset if needed
if env.done:
print(agent.check_memory(batch_size))
if type(agent.current_agent) == RandomAgent:
agent_type = PigChaseEnvironment.AGENT_TYPE_1
else:
agent_type = PigChaseEnvironment.AGENT_TYPE_2
##Aqui el state habria que modificarlo de nuevo
if num_actions > batch_size:
print('Entrando a replay 1')
agent.replay(batch_size)
state = env.reset(agent_type)
# select an action
#print('Accion del role 1')
action = agent.act(state, reward, agent_done, is_training=True)
next_state, reward, agent_done = env.do(action)
num_actions = num_actions + 1
next_state2 = adapt_state(next_state)
agent.remember(state, action, reward, next_state2, agent_done)
##Aqui state= obs (que seria el estado anterior estado modificado)
state = next_state
##No estoy seguro de si esto va aqui por el while true (no se cuando acaba). Deberia ir cuando acaba una partida
##Hacer check si hace el replay o no. Si no lo hace nunca, meter el replay dentro de el if(env.done (signifca que una etapa ha acabado y empieza otra, por lo que deberia esta bien))
else:
if baseline_agent == 'astar':
agent = FocusedAgent(name, ENV_TARGET_NAMES[0])
else:
agent = RandomAgent(name, env.available_actions)
state = env.reset()
reward = 0
agent_done = False
viz_rewards = []
max_training_steps = EPOCH_SIZE * max_epochs
for step in six.moves.range(1, max_training_steps + 1):
# check if env needs reset
if env.done:
visualize_training(visualizer, step, viz_rewards)
viz_rewards = []
##No se si esto se tiene que hacer tambien aqui o no, hacer check
if agent.check_memory(batch_size) > batch_size:
print('Entrando a replay 2')
agent.replay(batch_size)
state = env.reset()
# select an action
#print('Accion del role 2')
action = agent.act(state, reward, agent_done, is_training=True)
# take a step
next_state, reward, agent_done = env.do(action)
next_state2 = adapt_state(next_state)
agent.remember(state, action, reward, next_state2, agent_done)
##Aqui state= obs (que seria el estado anterior estado modificado)
state = next_state
#obs, reward, agent_done = env.do(action)
viz_rewards.append(reward)
agent.inject_summaries(step)
def agent_factory(name, role, clients, backend, device, max_epochs, logdir,
visualizer):
assert len(clients) >= 2, 'Not enough clients (need at least 2)'
clients = parse_clients_args(clients)
if role == 0:
builder = PigChaseSymbolicStateBuilder()
env = PigChaseEnvironment(clients,
builder,
role=role,
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
agent_done = False
while True:
if env.done:
if type(agent.current_agent) == RandomAgent:
agent_type = PigChaseEnvironment.AGENT_TYPE_1
else:
agent_type = PigChaseEnvironment.AGENT_TYPE_2
obs = env.reset(agent_type)
while obs is None:
# this can happen if the episode ended with the first
# action of the other agent
print('Warning: received obs == None.')
obs = env.reset(agent_type)
# select an action
#Aqui seleccionamos la acción a tomar por el agente 0
action = agent.act(obs, reward, agent_done, is_training=True)
# take a step
#aquí estamos en el bucle de realizar la acción
obs, reward, agent_done = env.do(action)
else:
env = PigChaseEnvironment(clients,
MalmoALEStateBuilder(),
role=role,
randomize_positions=True)
memory = TemporalMemory(100000, (84, 84))
if backend == 'cntk':
from malmopy.model.cntk import QNeuralNetwork
model = QNeuralNetwork((memory.history_length, 84, 84),
env.available_actions, device)
else:
from malmopy.model.chainer import QNeuralNetwork, DQNChain
chain = DQNChain((memory.history_length, 84, 84),
env.available_actions)
target_chain = DQNChain((memory.history_length, 84, 84),
env.available_actions)
model = QNeuralNetwork(chain, target_chain, device)
explorer = LinearEpsilonGreedyExplorer(1, 0.1, 1000000)
agent = PigChaseQLearnerAgent(name,
env.available_actions,
model,
memory,
0.99,
32,
50000,
explorer=explorer,
visualizer=visualizer)
obs = env.reset()
reward = 0
agent_done = False
viz_rewards = []
max_training_steps = EPOCH_SIZE * max_epochs
for step in six.moves.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 = []
obs = env.reset()
while obs is None:
# this can happen if the episode ended with the first
# action of the other agent
print('Warning: received obs == None.')
obs = env.reset()
# select an action
action = agent.act(obs, reward, agent_done, is_training=True)
# take a step
obs, reward, agent_done = env.do(action)
viz_rewards.append(reward)
if (step % EPOCH_SIZE) == 0:
if 'model' in locals():
model.save('pig_chase-dqn_%d.model' % (step / EPOCH_SIZE))
def agent_factory(name, role, type, clients, max_epochs, logdir, visualizer):
assert len(clients) >= 2, 'Not enough clients (need at least 2)'
clients = parse_clients_args(clients)
builder = PigChaseSymbolicStateBuilder()
env = PigChaseEnvironment(clients,
builder,
role=role,
randomize_positions=True)
if role == 0:
agent = FocusedAgent(name, ENV_TARGET_NAMES[0])
################### should add other two agent
obs = env.reset()
reward = 0
agent_done = False
max_training_steps = EPOCH_SIZE * max_epochs
epoch = 0
for step in range(1, max_training_steps + 1):
if env.done:
obs = env.reset()
epoch += 1
# select an action
action = agent.act(obs, reward, agent_done)
# take a step
obs, reward, agent_done = env.do(action)
else:
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
agent = BayesAgent(name, ENV_TARGET_NAMES[0], 'Agent_1', True,
sess)
if not agent.save:
sess.run(tf.global_variables_initializer())
print "Initialize"
obs = env.reset()
agent.reset(obs)
reward = 0
agent_done = False
viz_rewards = []
avg = []
epoch = 0
s = 1
max_training_steps = EPOCH_SIZE * max_epochs
for step in range(1, max_training_steps + 1):
# check if env needs reset
if agent_done:
obs = env.reset()
agent.reset(obs)
avg.append(sum(viz_rewards))
print "Epoch:%d, accumulative rewards: %d" % (
epoch, sum(viz_rewards))
visualize_training(visualizer, step, viz_rewards)
viz_rewards = []
epoch += 1
if epoch % 10 == 0:
agent.reset_collaborator()
s = 1
# select an action
action = agent.act(obs, reward, agent_done, is_training=True)
# take a step
next_obs, reward, agent_done = env.do(action)
agent.training(obs, action, reward, next_obs, agent_done, s,
step)
s += 1
obs = next_obs
viz_rewards.append(reward)
if step % 100 == 0:
agent.save_replay_buffer()
#
agent.inject_summaries(step)
print "Average Reward: ", 1. * sum(avg) / len(avg)
def agent_factory(name, role, baseline_agent, clients, max_epochs, logdir,
visualizer):
assert len(clients) >= 2, 'Not enough clients (need at least 2)'
clients = parse_clients_args(clients)
builder = PigChaseSymbolicStateBuilder()
env = PigChaseEnvironment(clients,
builder,
role=role,
randomize_positions=True)
if role == 0:
agent = PigChaseChallengeAgent(name)
obs = env.reset(get_agent_type(agent))
reward = 0
agent_done = False
while True:
if env.done:
while True:
obs = env.reset(get_agent_type(agent))
if obs:
break
# select an action
action = agent.act(obs, reward, agent_done, is_training=True)
# reset if needed
if env.done:
obs = env.reset(get_agent_type(agent))
# take a step
obs, reward, agent_done = env.do(action)
else:
if baseline_agent == 'tabq':
agent = TabularQLearnerAgent(name, visualizer)
elif baseline_agent == 'astar':
agent = FocusedAgent(name, ENV_TARGET_NAMES[0])
else:
agent = RandomAgent(name, env.available_actions)
obs = env.reset()
reward = 0
agent_done = False
viz_rewards = []
max_training_steps = EPOCH_SIZE * max_epochs
for step in six.moves.range(1, max_training_steps + 1):
# check if env needs reset
if env.done:
while True:
if len(viz_rewards) == 0:
viz_rewards.append(0)
visualize_training(visualizer, step, viz_rewards)
tag = "Episode End Conditions"
visualizer.add_entry(
step, '%s/timeouts per episode' % tag,
env.end_result == "command_quota_reached")
visualizer.add_entry(
step, '%s/agent_1 defaults per episode' % tag,
env.end_result == "Agent_1_defaulted")
visualizer.add_entry(
step, '%s/agent_2 defaults per episode' % tag,
env.end_result == "Agent_2_defaulted")
visualizer.add_entry(step,
'%s/pig caught per episode' % tag,
env.end_result == "caught_the_pig")
agent.inject_summaries(step)
viz_rewards = []
obs = env.reset()
if obs:
break
# select an action
action = agent.act(obs, reward, agent_done, is_training=True)
# take a step
obs, reward, agent_done = env.do(action)
viz_rewards.append(reward)