def run_dqn(**kargs):
if kargs['output_dir'] is None and kargs['logdir'] is not None:
kargs['output_dir'] = kargs['logdir']
q_model_initial = kargs[
'q_model_initial'] if 'q_model_initial' in kargs else None
from collections import namedtuple
args = namedtuple("DQNParams", kargs.keys())(*kargs.values())
if 'dont_init_tf' not in kargs.keys() or not kargs['dont_init_tf']:
#init_nn_library(True, "1")
init_nn_library("gpu" in kargs and kargs["gpu"] is not None,
kargs["gpu"] if "gpu" in kargs else "1")
#if args.atari:
# env = gym_env(args.game + 'NoFrameskip-v0')
# env = WarmUp(env, min_step=0, max_step=30)
# env = ActionRepeat(env, 4)
# #q_model = A3CModel(modelOps)
#else:
# if args.game == "Grid":
# env = GridEnv()
# else:
# env = gym_env(args.game)
# #q_model = TabularQModel(modelOps)
#for trans in args.env_transforms:
# env = globals()[trans](env)
if 'use_env' in kargs and kargs['use_env'] is not None:
env = kargs['use_env']
else:
env = get_env(args.game, args.atari, args.env_transforms,
kargs['monitor_dir'] if 'monitor_dir' in kargs else None)
if 'env_model' in kargs and kargs['env_model'] is not None and kargs[
'env_weightfile'] is not None:
print('Using simulated environment')
envOps = EnvOps(env.observation_space.shape, env.action_space.n,
args.learning_rate)
env_model = globals()[kargs['env_model']](envOps)
env_model.model.load_weights(kargs['env_weightfile'])
env = SimulatedEnv(env,
env_model,
use_reward='env_reward' in kargs
and kargs['env_reward'])
modelOps = DqnOps(env.action_count)
modelOps.dueling_network = args.dueling_dqn
viewer = None
if args.enable_render:
viewer = EnvViewer(env, args.render_step, 'human')
if args.atari:
proproc = PreProPipeline(
[GrayPrePro(), ResizePrePro(modelOps.INPUT_SIZE)])
rewproc = PreProPipeline([RewardClipper(-1, 1)])
else:
if env.observation_space.__class__.__name__ is 'Discrete':
modelOps.INPUT_SIZE = env.observation_space.n
else:
modelOps.INPUT_SIZE = env.observation_space.shape
modelOps.AGENT_HISTORY_LENGTH = 1
proproc = None
rewproc = None
modelOps.LEARNING_RATE = args.learning_rate
if q_model_initial is None:
q_model = globals()[args.model](modelOps)
else:
q_model = q_model_initial
if not args.load_weightfile is None:
q_model.model.load_weights(args.load_weightfile)
summary_writer = tf.summary.FileWriter(
args.logdir,
K.get_session().graph) if not args.logdir is None else None
agentOps = DqnAgentOps()
agentOps.double_dqn = args.double_dqn
agentOps.mode = args.mode
if args.mode == "train":
agentOps.TARGET_NETWORK_UPDATE_FREQUENCY = args.target_network_update
replay_buffer = None
if args.replay_buffer_size > 0:
if 'load_trajectory' in kargs and kargs['load_trajectory'] is not None:
replay_buffer = TrajectoryReplay(kargs['load_trajectory'],
kargs['batch_size'],
args.update_frequency,
args.replay_start_size)
else:
replay_buffer = ReplayBuffer(args.replay_buffer_size,
modelOps.AGENT_HISTORY_LENGTH,
args.update_frequency,
args.replay_start_size,
args.batch_size)
#replay_buffer = NStepBuffer(modelOps.AGENT_HISTORY_LENGTH, 8)
agent = DqnAgent(env.action_space, q_model, replay_buffer, rewproc,
agentOps, summary_writer)
egreedyOps = EGreedyOps()
if replay_buffer is not None:
egreedyOps.REPLAY_START_SIZE = replay_buffer.REPLAY_START_SIZE
egreedyOps.mode = args.mode
egreedyOps.test_epsilon = args.test_epsilon
#egreedyOps.FINAL_EXPLORATION_FRAME = 10000
if args.mode == "train":
egreedyOps.FINAL_EXPLORATION_FRAME = args.egreedy_final_step
if args.mode == "train":
if args.egreedy_decay < 1:
egreedyOps.DECAY = args.egreedy_decay
egreedyAgent = EGreedyAgentExp(env.action_space, egreedyOps, agent)
else:
egreedyAgent = MultiEGreedyAgent(
env.action_space,
egreedyOps,
agent,
args.egreedy_props,
args.egreedy_final,
final_exp_frame=args.egreedy_final_step)
else:
egreedyAgent = EGreedyAgent(env.action_space, egreedyOps, agent)
runner = Runner(env,
egreedyAgent,
proproc,
modelOps.AGENT_HISTORY_LENGTH,
max_step=args.max_step,
max_episode=args.max_episode)
if replay_buffer is not None:
runner.listen(replay_buffer, proproc)
runner.listen(agent, None)
runner.listen(egreedyAgent, None)
if viewer is not None:
runner.listen(viewer, None)
if args.output_dir is not None:
networkSaver = NetworkSaver(
50000 if 'save_interval' not in kargs else kargs['save_interval'],
args.output_dir, q_model.model)
runner.listen(networkSaver, None)
return runner, agent
agentOps = DqnAgentOps()
agentOps.double_dqn = args.double_dqn
agentOps.TARGET_NETWORK_UPDATE_FREQUENCY = 20
#agentOps.REPLAY_START_SIZE = 100
#agentOps.FINAL_EXPLORATION_FRAME = 10000
replay_buffer = ReplayBuffer(int(2000), 1, 1, 1000, 64)
#replay_buffer = NStepBuffer(modelOps.AGENT_HISTORY_LENGTH, 8)
agent = DqnAgent(env.action_space, q_model, replay_buffer, rewproc, agentOps,
summary_writer)
egreedyOps = EGreedyOps()
egreedyOps.REPLAY_START_SIZE = replay_buffer.REPLAY_START_SIZE
egreedyOps.FINAL_EXPLORATION_FRAME = 10000
egreedyOps.FINAL_EXPLORATION = 0.01
egreedyOps.DECAY = 0.999
egreedyAgent = EGreedyAgentExp(env.action_space, egreedyOps, agent)
runner = Runner(env, egreedyAgent, proproc, 1)
runner.listen(replay_buffer, proproc)
runner.listen(agent, None)
runner.listen(egreedyAgent, None)
if viewer is not None:
runner.listen(viewer, None)
if args.logdir is not None:
networkSaver = NetworkSaver(50000, args.logdir, q_model.model)
runner.listen(networkSaver, None)
runner.run()
class AgentThread(StoppableThread, RunnerListener):
def __init__(self, threadId, sess, graph):
StoppableThread.__init__(self)
self.threadId = threadId
self.sess = sess
self.graph = graph
with self.graph.as_default():
if args.game == "Grid":
env = GridEnv()
else:
env = gym_env(args.game)
env = Penalizer(env)
proproc = None
rewproc = None
q_model = CartPoleModel(modelOps)
q_model.model_update = model.model
q_model.set_weights(model.get_weights())
summary_writer = tf.summary.FileWriter(
args.logdir + '/thread-' + str(threadId),
K.get_session().graph) if not args.logdir is None else None
agentOps = DqnAgentOps()
agentOps.double_dqn = args.double_dqn
agentOps.TARGET_NETWORK_UPDATE_FREQUENCY = 20
agentOps.REPLAY_START_SIZE = 1
#agentOps.INITIAL_EXPLORATION = 0
agentOps.TARGET_NETWORK_UPDATE_FREQUENCY = 1e10
#replay_buffer = ReplayBuffer(int(1e6), 4, 4, agentOps.REPLAY_START_SIZE, 32)
replay_buffer = NStepBuffer(modelOps.AGENT_HISTORY_LENGTH,
args.nstep)
agent = DqnAgent(env.action_space,
q_model,
replay_buffer,
rewproc,
agentOps,
summary_writer,
model_eval=model_eval) #
egreedyOps = EGreedyOps()
egreedyOps.REPLAY_START_SIZE = 1
egreedyOps.FINAL_EXPLORATION_FRAME = 5000
egreedyOps.FINAL_EXPLORATION = 0.01
egreedyOps.DECAY = 0.999
egreedyAgent = MultiEGreedyAgent(env.action_space, egreedyOps,
agent, [0.4, 0.3, 0.3],
[0.1, 0.01, 0.5])
self.runner = Runner(env, egreedyAgent, proproc,
modelOps.AGENT_HISTORY_LENGTH)
self.runner.listen(replay_buffer, proproc)
self.runner.listen(agent, None)
self.runner.listen(egreedyAgent, None)
self.runner.listen(self, proproc)
pass
def run(self):
with self.graph.as_default():
self.runner.run()
def on_step(self, ob, action, next_ob, reward, done):
global T
global model, model_eval
with tLock:
T = T + 1
#if T % 1000 == 0:
# print('STEP', T)
if T % target_network_update_freq == 0:
print('CLONE TARGET')
model_eval.set_weights(model.get_weights())
for agent in agents:
agent.model_eval = model_eval
if T % args.render_step == 0 and ENABLE_RENDER:
viewer.imshow(
np.repeat(np.reshape(ob, ob.shape + (1, )), 3, axis=2))
if T % SAVE_FREQ == 0 and args.mode == "train":
if not args.output_dir is None:
model.model.save_weights(args.output_dir +
'/weights_{0}.h5'.format(T))
#print(T)
def stop(self):
super(AgentThread, self).stop()
self.runner.stop()
class AgentThread(StoppableThread, RunnerListener):
def __init__(self, threadId, sess, graph):
StoppableThread.__init__(self)
self.threadId = threadId
self.sess = sess
self.graph = graph
with self.graph.as_default():
if args.atari:
env = gym_env(args.game + 'NoFrameskip-v0')
env = WarmUp(env, min_step=0, max_step=30)
env = ActionRepeat(env, 4)
proproc = PreProPipeline(
[GrayPrePro(),
ResizePrePro(modelOps.INPUT_SIZE)])
rewproc = PreProPipeline([RewardClipper(-1, 1)])
#q_model = A3CModel(modelOps)
else:
if args.game == "Grid":
env = GridEnv()
else:
env = gym_env(args.game)
proproc = None
rewproc = None
#q_model = TabularQModel(modelOps)
for trans in args.env_transforms:
env = globals()[trans](env)
if 'shared_model' in kargs and kargs['shared_model']:
q_model = model
else:
q_model = globals()[args.model](modelOps)
q_model.model_update = model.model
q_model.set_weights(model.get_weights())
summary_writer = tf.summary.FileWriter(
args.logdir + '/thread-' + str(threadId),
K.get_session().graph) if not args.logdir is None else None
agentOps = DqnAgentOps()
agentOps.double_dqn = args.double_dqn
agentOps.REPLAY_START_SIZE = 1
#agentOps.INITIAL_EXPLORATION = 0
agentOps.TARGET_NETWORK_UPDATE_FREQUENCY = 1e10
#replay_buffer = ReplayBuffer(int(1e6), 4, 4, agentOps.REPLAY_START_SIZE, 32)
replay_buffer = None
#if threadId > 0:
if args.nstep > 0:
replay_buffer = NStepBuffer(modelOps.AGENT_HISTORY_LENGTH,
args.nstep)
else:
replay_buffer = ReplayBuffer(args.replay_buffer_size,
modelOps.AGENT_HISTORY_LENGTH,
args.update_frequency,
args.replay_start_size,
args.batch_size)
#print(kargs['agent'])
if kargs['agent'] == 'ActorCriticAgent':
agent = ActorCriticAgent(env.action_space,
q_model,
replay_buffer,
rewproc,
agentOps,
summary_writer,
ac_model_update=model) #
else:
agent = DqnAgent(env.action_space,
q_model,
replay_buffer,
rewproc,
agentOps,
summary_writer,
model_eval=model_eval) #
egreedyAgent = None
if threadId > 0 and kargs[
'agent'] != 'ActorCriticAgent': # first thread is for testing
egreedyOps = EGreedyOps()
egreedyOps.REPLAY_START_SIZE = replay_buffer.REPLAY_START_SIZE
#egreedyOps.FINAL_EXPLORATION_FRAME = int(args.egreedy_final_step / args.thread_count)
#if args.egreedy_decay<1:
# egreedyAgent = EGreedyAgentExp(env.action_space, egreedyOps, agent)
#else:
if len(args.egreedy_props
) > 1 and args.egreedy_props[0] == round(
args.egreedy_props[0]):
cs = np.array(args.egreedy_props)
cs = np.cumsum(cs)
idx = np.searchsorted(cs, threadId)
print('Egreedyagent selected', idx,
args.egreedy_final[idx], args.egreedy_decay[idx],
args.egreedy_final_step[idx])
egreedyAgent = MultiEGreedyAgent(
env.action_space, egreedyOps, agent, [1],
[args.egreedy_final[idx]],
[args.egreedy_decay[idx]],
[args.egreedy_final_step[idx]])
else:
egreedyAgent = MultiEGreedyAgent(
env.action_space, egreedyOps, agent,
args.egreedy_props, args.egreedy_final,
args.egreedy_decay, args.egreedy_final_step)
self.runner = Runner(
env, egreedyAgent if egreedyAgent is not None else agent,
proproc, modelOps.AGENT_HISTORY_LENGTH)
if replay_buffer is not None:
self.runner.listen(replay_buffer, proproc)
self.runner.listen(agent, None)
if egreedyAgent is not None:
self.runner.listen(egreedyAgent, None)
self.runner.listen(self, proproc)
self.agent = agent
self.q_model = q_model
pass
def run(self):
with self.graph.as_default():
self.runner.run()
def on_step(self, ob, action, next_ob, reward, done):
global T
global model, model_eval
with tLock:
T = T + 1
if T % target_network_update_freq == 0 and kargs[
'agent'] != 'ActorCriticAgent':
#print('CLONE TARGET: ' + str(T))
model_eval.set_weights(model.get_weights())
for agent in agents:
agent.model_eval = model_eval
if T % SAVE_FREQ == 0 and args.mode == "train":
if not args.output_dir is None:
model.model.save_weights(args.output_dir +
'/weights_{0}.h5'.format(T))
#if T % 1000 == 0:
# print('STEP', T)
#if self.threadId == 0 and T % 10 == 0:
# self.q_model.set_weights(model.get_weights())
if T % args.render_step == 0 and ENABLE_RENDER:
viewer.imshow(
np.repeat(np.reshape(ob, ob.shape + (1, )), 3, axis=2))
if T > args.max_step:
self.stop()
#print(T)
def stop(self):
super(AgentThread, self).stop()
self.runner.stop()
#print(env.observation_space.n) modelOps = DqnOps(env.action_count) modelOps.dueling_network = args.dueling_dqn modelOps.INPUT_SIZE = env.observation_space.n modelOps.LEARNING_RATE = 0.2 q_model = TabularQModel(modelOps) summary_writer = tf.summary.FileWriter(args.logdir, K.get_session().graph) if not args.logdir is None else None agentOps = DqnAgentOps() agentOps.double_dqn = args.double_dqn replay_buffer = NStepBuffer(1, args.nstep) agent = DqnAgent(env.action_space, q_model, replay_buffer, None, agentOps, summary_writer) egreedyOps = EGreedyOps() egreedyOps.REPLAY_START_SIZE = 1 egreedyOps.FINAL_EXPLORATION_FRAME = 10000 egreedyAgent = EGreedyAgent(env.action_space, egreedyOps, agent) runner = Runner(env, egreedyAgent, None, 1) runner.listen(replay_buffer, None) runner.listen(agent, None) runner.listen(egreedyAgent, None) runner.run()
