def train(args):
# Init gym env
env = Environment(args)
agent = Q_Agent(env, args)
stats = Statistics(None, agent, env, args)
if not args.load_model.isspace(): agent.loadModel(args.load_model)
# Train agent
try:
for epoch in range(args.epochs):
print 'Epoch #%d' % (epoch + 1)
if args.train_steps > 0:
print 'Training for %d steps' % args.train_steps
agent.train(args.train_steps)
stats.write(epoch + 1, 'train', tensorboard=False)
if args.test_eps > 0:
print 'Testing for %d steps' % args.test_eps
agent.test(args.test_eps, render=args.render)
stats.write(epoch + 1, 'test', tensorboard=False)
agent.saveModel(args.job_name)
stats.plot()
stats.close()
agent.test(10, render=True)
print 'Done'
except KeyboardInterrupt:
agent.saveModel(args.job_name)
stats.plot()
stats.close()
agent.test(10, render=True)
print 'Caught keyboard interrupt, stopping run...'
def test(args):
with tf.Session() as sess:
# Init environment and agent
env = Environment(args)
agent = DQN_Agent(sess, env, args)
stats = Statistics(sess, agent, env, args)
if args.load_model: stats.loadModel()
sess.graph.finalize()
try:
print 'Taking %d random actions before training' % args.steps_pre_train
agent.randomExplore(args.steps_pre_train)
agent.startEnqueueThreads()
for epoch in range(args.epochs):
print 'Epoch #%d' % (epoch + 1)
if args.test_eps > 0:
print 'Testing for %d episodes' % args.test_eps
agent.test(args.test_eps, render=True)
stats.write(epoch + 1, 'test')
agent.stopEnqueueThreads()
stats.plot()
stats.close()
print 'Done'
except KeyboardInterrupt:
stats.plot()
stats.close()
agent.stopEnqueueThreads()
print 'Caught keyboard interrupt, stopping run...'
def main():
# Process arguments
args = utils.parse_args()
# Use random seed from argument
if args.random_seed:
random.seed(args.random_seed)
# Instantiate environment class
if args.environment == "ale":
env = ALEEnvironment(args.game, args)
elif args.environment == "gym":
env = GymEnvironment(args.game, args)
elif args.environment == "robot":
env = RobotEnvironment(args.game, args)
else:
assert False, "Unknown environment" + args.environment
# Instantiate DQN
action_dim = env.action_dim()
state_dim = env.state_dim()
net = DQN(state_dim, action_dim, args)
# Load weights before starting training
if args.load_weights:
filepath = args.load_weights
net.load(filepath)
# Instantiate agent
agent = Agent(env, net, args)
# Start statistics
stats = Statistics(agent, agent.net, agent.net.memory, env, args)
# Play game with two players (user and agent)
if args.two_player:
player_b = PlayerTwo(args)
env.set_mode('test')
stats.reset()
agent.play_two_players(player_b)
stats.write(0, "2player")
sys.exit()
# Play agent
if args.play_games > 0:
env.set_mode('test')
stats.reset()
for _ in range(args.play_games):
agent.play()
stats.write(0, "play")
sys.exit()
# Populate replay memory with random steps
if args.random_steps:
env.set_mode('test')
stats.reset()
agent.play_random(args.random_steps)
stats.write(0, "random")
for epoch in range(args.start_epoch, args.epochs):
# Train agent
if args.train_steps:
env.set_mode('train')
stats.reset()
agent.train(args.train_steps)
stats.write(epoch + 1, "train")
# Save weights after every epoch
if args.save_weights_prefix:
filepath = args.save_weights_prefix + "_%d.h5" % (epoch + 1)
net.save(filepath)
# Test agent
if args.test_steps:
env.set_mode('test')
stats.reset()
agent.test(args.test_steps)
stats.write(epoch + 1, "test")
# Stop statistics
stats.close()
agent.play_random(args.random_steps, args)
stats.write(0, "random")
# loop over epochs
for epoch in range(args.start_epoch, args.epochs):
logger.info("Epoch #%d" % (epoch + 1))
if args.train_steps:
logger.info(" Training for %d steps" % args.train_steps)
# Set env mode test so that loss of life is considered as terminal
env.setMode('train')
stats.reset()
agent.train(args.train_steps, epoch)
stats.write(epoch + 1, "train")
if args.save_weights_prefix:
filename = args.save_weights_prefix + "_%d.prm" % (epoch + 1)
logger.info("Saving weights to %s" % filename)
net.save_weights(filename)
if args.test_steps:
logger.info(" Testing for %d steps" % args.test_steps)
# Set env mode test so that loss of life is not considered as terminal
env.setMode('test')
stats.reset()
agent.test(args.test_steps, epoch)
stats.write(epoch + 1, "test")
stats.close()
logger.info("All done")
if args.random_steps:
# populate replay memory with random steps
logger.info("Populating replay memory with %d random moves" % args.random_steps)
stats.reset()
agent.play_random(args.random_steps)
stats.write(0, "random")
# loop over epochs
for epoch in xrange(args.epochs):
logger.info("Epoch #%d" % (epoch + 1))
if args.train_steps:
logger.info(" Training for %d steps" % args.train_steps)
stats.reset()
agent.train(args.train_steps, epoch)
stats.write(epoch + 1, "train")
if args.save_weights_prefix:
filename = args.save_weights_prefix + "_%d.prm" % (epoch + 1)
logger.info("Saving weights to %s" % filename)
net.save_weights(filename)
if args.test_steps:
logger.info(" Testing for %d steps" % args.test_steps)
stats.reset()
agent.test(args.test_steps, epoch)
stats.write(epoch + 1, "test")
stats.close()
logger.info("All done")
