def main():
"""Run DQN until the environment throws an exception."""
env = make(game='SonicTheHedgehog-Genesis', state='GreenHillZone.Act1')
env = AllowBacktracking(make_local_env(env, stack=False, scale_rew=False))
env = BatchedFrameStack(BatchedGymEnv([[env]]), num_images=4, concat=False)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True # pylint: disable=E1101
with tf.Session(config=config) as sess:
dqn = DQN(*rainbow_models(sess,
env.action_space.n,
gym_space_vectorizer(env.observation_space),
min_val=-200,
max_val=200))
player = NStepPlayer(BatchedPlayer(env, dqn.online_net), 3)
optimize = dqn.optimize(learning_rate=1e-4)
sess.run(tf.global_variables_initializer())
dqn.train(num_steps=num_steps, # Make sure an exception arrives before we stop.
player=player,
replay_buffer=PrioritizedReplayBuffer(500000, 0.5, 0.4, epsilon=0.1),
optimize_op=optimize,
train_interval=1,
target_interval=8192,
batch_size=32,
min_buffer_size=20000)
print(tf.trainable_variables())
save_path='/home/noob/retro-noob/rainbow/params/params'
utils.save_state(save_path+'_tf_saver')
with tf.variable_scope('model'):
params = tf.trainable_variables()
ps = sess.run(params)
joblib.dump(ps, save_path + '_joblib')
def main():
"""Run DQN until the environment throws an exception."""
env = make(game='SonicTheHedgehog-Genesis', state='GreenHillZone.Act1')
env = BatchedFrameStack(BatchedGymEnv([[env]]), num_images=4, concat=False)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True # pylint: disable=E1101
with tf.Session(config=config) as sess:
dqn = DQN(*rainbow_models(sess,
env.action_space.n,
gym_space_vectorizer(env.observation_space),
min_val=-200,
max_val=200))
player = NStepPlayer(BatchedPlayer(env, dqn.online_net), 3)
optimize = dqn.optimize(learning_rate=1e-4)
sess.run(tf.global_variables_initializer())
dqn.train(
num_steps=2000000, # Make sure an exception arrives before we stop.
player=player,
replay_buffer=PrioritizedReplayBuffer(500000,
0.5,
0.4,
epsilon=0.1),
optimize_op=optimize,
train_interval=1,
target_interval=8192,
batch_size=32,
min_buffer_size=20000)
def main():
"""Run DQN until the environment throws an exception."""
base_path = "results/rainbow/6/"
env = make_env(stack=False, scale_rew=False, render=None, monitor=base_path + "train_monitor",
episodic_life=True)
# I think the env itself allows Backtracking
env = BatchedFrameStack(BatchedGymEnv([[env]]), num_images=4, concat=False)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.gpu_options.per_process_gpu_memory_fraction = 0.8
with tf.Session(config=config) as sess:
dqn = DQN(*rainbow_models(sess,
env.action_space.n, gym_space_vectorizer(env.observation_space),
min_val=-200, max_val=200))
player = NStepPlayer(BatchedPlayer(env, dqn.online_net), 3)
optimize = dqn.optimize(learning_rate=1e-4)
saver = tf.train.Saver(name="rainbow")
sess.run(tf.global_variables_initializer())
saver.save(sess, base_path + "training", global_step=0)
try:
dqn.train(num_steps=2_000_000, # Make sure an exception arrives before we stop.
player=player,
replay_buffer=PrioritizedReplayBuffer(500000, 0.5, 0.4, epsilon=0.1),
optimize_op=optimize,
train_interval=1,
target_interval=8192,
batch_size=64,
min_buffer_size=20000,
handle_ep=handle_ep) # in seconds
except KeyboardInterrupt:
print("keyboard interrupt")
print("finishing")
saver.save(sess, base_path + "final", global_step=2_000_000)
def main():
"""Run DQN until the environment throws an exception."""
env = AllowBacktracking(make_env(stack=False, scale_rew=False))
env = BatchedFrameStack(BatchedGymEnv([[env]]), num_images=4, concat=False)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True # pylint: disable=E1101
with tf.Session(config=config) as sess:
dqn = DQN(*rainbow_models(sess,
env.action_space.n,
gym_space_vectorizer(env.observation_space),
min_val=-200,
max_val=200))
player = NStepPlayer(BatchedPlayer(env, dqn.online_net), 3)
optimize = dqn.optimize(learning_rate=1e-4)
sess.run(tf.global_variables_initializer())
dqn.train(
num_steps=2000000, # Make sure an exception arrives before we stop.
player=player,
replay_buffer=StochasticMaxStochasticDeltaDeletionPRB(500000,
0.5,
0.4,
epsilon=0.1),
optimize_op=optimize,
train_interval=1,
target_interval=8192,
batch_size=32,
min_buffer_size=20000)
def main():
env = AllowBacktracking(make_env(stack=False, scale_rew=False))
env = BatchedFrameStack(BatchedGymEnv([[env]]), num_images=4, concat=False)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
dqn = DQN(*rainbow_models(sess,
env.action_space.n,
gym_space_vectorizer(env.observation_space),
min_val=-421,
max_val=421))
player = NStepPlayer(BatchedPlayer(env, dqn.online_net), 3)
optimize = dqn.optimize(learning_rate=1e-4)
sess.run(tf.global_variables_initializer())
dqn.train(num_steps=2000000,
player=player,
replay_buffer=PrioritizedReplayBuffer(500000,
0.5,
0.4,
epsilon=0.1),
optimize_op=optimize,
train_interval=1,
target_interval=64,
batch_size=32,
min_buffer_size=25000)
def main():
"""
Entry-point for the program.
"""
env = gym.make('CartPole-v0')
with tf.Session() as sess:
make_net = lambda name: MLPQNetwork(sess,
env.action_space.n,
gym_space_vectorizer(
env.observation_space),
name,
layer_sizes=[32])
dqn = DQN(make_net('online'), make_net('target'))
player = BasicPlayer(env,
EpsGreedyQNetwork(dqn.online_net, EPSILON),
batch_size=STEPS_PER_UPDATE)
optimize = dqn.optimize(learning_rate=LEARNING_RATE)
sess.run(tf.global_variables_initializer())
dqn.train(num_steps=30000,
player=player,
replay_buffer=UniformReplayBuffer(BUFFER_SIZE),
optimize_op=optimize,
target_interval=200,
batch_size=64,
min_buffer_size=200,
handle_ep=lambda _, rew: print('got reward: ' + str(rew)))
env.close()
def main():
"""Run DQN until the environment throws an exception."""
env = AllowBacktracking(make_env(stack=False, scale_rew=False))
env = BatchedFrameStack(BatchedGymEnv([[env]]), num_images=4, concat=False)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True # pylint: disable=E1101
with tf.Session(config=config) as sess:
dqn = DQN(*rainbow_models(sess,
env.action_space.n,
gym_space_vectorizer(env.observation_space),
min_val=-200,
max_val=200))
player = NStepPlayer(BatchedPlayer(env, dqn.online_net), 3)
"""
Create a TF Op that optimizes the objective.
Args:
learning_rate: the Adam learning rate.
epsilon: the Adam epsilon.
"""
optimize = dqn.optimize(learning_rate=6.25e-5, epsilon=1.5e-4)
sess.run(tf.global_variables_initializer())
"""
Run an automated training loop.
This is meant to provide a convenient way to run a
standard training loop without any modifications.
You may get more flexibility by writing your own
training loop.
Args:
num_steps: the number of timesteps to run.
player: the Player for gathering experience.
replay_buffer: the ReplayBuffer for experience.
optimize_op: a TF Op to optimize the model.
train_interval: timesteps per training step.
target_interval: number of timesteps between
target network updates.
batch_size: the size of experience mini-batches.
min_buffer_size: minimum replay buffer size
before training is performed.
tf_schedules: a sequence of TFSchedules that are
updated with the number of steps taken.
handle_ep: called with information about every
completed episode.
timeout: if set, this is a number of seconds
after which the training loop should exit.
"""
dqn.train(
num_steps=1000000, # Make sure an exception arrives before we stop.
player=player,
replay_buffer=PrioritizedReplayBuffer(500000,
0.5,
0.4,
epsilon=0.1),
optimize_op=optimize,
train_interval=1,
target_interval=8192,
batch_size=32,
min_buffer_size=20000)
def main():
"""Run DQN until the environment throws an exception."""
env = AllowBacktracking(make_env(stack=False, scale_rew=False))
env = BatchedFrameStack(BatchedGymEnv([[env]]), num_images=4, concat=False)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True # pylint: disable=E1101
with tf.Session(config=config) as sess:
dqn = DQN(*rainbow_models(sess,
env.action_space.n,
gym_space_vectorizer(env.observation_space),
min_val=-200,
max_val=200))
player = NStepPlayer(BatchedPlayer(env, dqn.online_net), 3)
# Other exploration schedules
#eps_decay_sched = LinearTFSchedule(50000, 1.0, 0.01)
#player = NStepPlayer(BatchedPlayer(env, EpsGreedyQNetwork(dqn.online_net, 0.1)), 3)
#player = NStepPlayer(BatchedPlayer(env, EpsGreedyQNetwork(dqn.online_net, TFScheduleValue(sess, eps_decay_sched))), 3)
#player = NStepPlayer(BatchedPlayer(env, SonicEpsGreedyQNetwork(dqn.online_net, TFScheduleValue(sess, eps_decay_sched))), 3)
optimize = dqn.optimize(learning_rate=1e-4)
sess.run(tf.global_variables_initializer())
reward_hist = []
total_steps = 0
def _handle_ep(steps, rew, env_rewards):
nonlocal total_steps
total_steps += steps
reward_hist.append(rew)
if total_steps % 10 == 0:
print('%d episodes, %d steps: mean of last 100 episodes=%f' %
(len(reward_hist), total_steps,
sum(reward_hist[-100:]) / len(reward_hist[-100:])))
dqn.train(
num_steps=2000000, # Make sure an exception arrives before we stop.
player=player,
replay_buffer=PrioritizedReplayBuffer(500000,
0.5,
0.4,
epsilon=0.1),
optimize_op=optimize,
train_interval=1,
target_interval=8192,
batch_size=32,
min_buffer_size=20000,
tf_schedules=[eps_decay_sched],
handle_ep=_handle_ep,
restore_path='./pretrained_model',
save_interval=None,
)
def main():
env_name = 'MineRLNavigateDense-v0'
"""Run DQN until the environment throws an exception."""
base_env = [SimpleNavigateEnvWrapper(get_env(env_name)) for _ in range(1)]
env = BatchedFrameStack(BatchedGymEnv([base_env]),
num_images=4,
concat=True)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True # pylint: disable=E1101
with tf.Session(config=config) as sess:
online, target = mine_rainbow_online_target(mine_cnn,
sess,
env.action_space.n,
gym_space_vectorizer(
env.observation_space),
min_val=-200,
max_val=200)
dqn = DQN(online, target)
player = NStepPlayer(BatchedPlayer(env, dqn.online_net), 3)
optimize = dqn.optimize(learning_rate=1e-4)
sess.run(tf.global_variables_initializer())
buffer_capacity = 5000
replay_buffer = PrioritizedReplayBuffer(buffer_capacity,
0.5,
0.4,
epsilon=0.1)
iter = non_bugged_data_arr(env_name, num_trajs=100)
expert_player = NStepPlayer(ImitationPlayer(iter, 200), 3)
for traj in expert_player.play():
replay_buffer.add_sample(traj, init_weight=1)
print('starting training')
dqn.train(num_steps=200,
player=player,
replay_buffer=replay_buffer,
optimize_op=optimize,
train_interval=1,
target_interval=8192,
batch_size=32,
min_buffer_size=20000)
print('starting eval')
player._cur_states = None
score = evaluate(player)
print(score)
def main():
"""
Entry-point for the program.
"""
args = _parse_args()
env = batched_gym_env([partial(make_single_env, args.game)] * args.workers)
# Using BatchedFrameStack with concat=False is more
# memory efficient than other stacking options.
env = BatchedFrameStack(env, num_images=4, concat=False)
with tf.Session() as sess:
def make_net(name):
return NatureQNetwork(sess,
env.action_space.n,
gym_space_vectorizer(env.observation_space),
name,
dueling=True)
dqn = DQN(make_net('online'), make_net('target'))
player = BatchedPlayer(env,
EpsGreedyQNetwork(dqn.online_net, args.epsilon))
optimize = dqn.optimize(learning_rate=args.lr)
sess.run(tf.global_variables_initializer())
reward_hist = []
total_steps = 0
def _handle_ep(steps, rew):
nonlocal total_steps
total_steps += steps
reward_hist.append(rew)
if len(reward_hist) == REWARD_HISTORY:
print('%d steps: mean=%f' %
(total_steps, sum(reward_hist) / len(reward_hist)))
reward_hist.clear()
dqn.train(num_steps=int(1e7),
player=player,
replay_buffer=UniformReplayBuffer(args.buffer_size),
optimize_op=optimize,
target_interval=args.target_interval,
batch_size=args.batch_size,
min_buffer_size=args.min_buffer_size,
handle_ep=_handle_ep)
env.close()
def main():
"""Run DQN until the environment throws an exception."""
env_fns, env_names = create_envs()
env = BatchedFrameStack(batched_gym_env(env_fns),
num_images=4,
concat=False)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True # pylint: disable=E1101
with tf.Session(config=config) as sess:
dqn = DQN(*rainbow_models(sess,
env.action_space.n,
gym_space_vectorizer(env.observation_space),
min_val=-200,
max_val=200))
player = NStepPlayer(BatchedPlayer(env, dqn.online_net), 3)
optimize = dqn.optimize(learning_rate=1e-4) # Use ADAM
sess.run(tf.global_variables_initializer())
reward_hist = []
total_steps = 0
def _handle_ep(steps, rew, env_rewards):
nonlocal total_steps
total_steps += steps
reward_hist.append(rew)
if total_steps % 1 == 0:
print('%d episodes, %d steps: mean of last 100 episodes=%f' %
(len(reward_hist), total_steps,
sum(reward_hist[-100:]) / len(reward_hist[-100:])))
dqn.train(
num_steps=
2000000000, # Make sure an exception arrives before we stop.
player=player,
replay_buffer=PrioritizedReplayBuffer(500000,
0.5,
0.4,
epsilon=0.1),
optimize_op=optimize,
train_interval=1,
target_interval=8192,
batch_size=32,
min_buffer_size=20000,
handle_ep=_handle_ep,
num_envs=len(env_fns),
save_interval=10,
)
def main():
"""Run DQN until the environment throws an exception."""
env = AllowBacktracking(make_env(stack=False, scale_rew=False))
env = BatchedFrameStack(BatchedGymEnv([[env]]), num_images=4, concat=False)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True # pylint: disable=E1101
with tf.Session(config=config) as sess:
dqn = DQN(*rainbow_models(sess,
env.action_space.n,
gym_space_vectorizer(env.observation_space),
min_val=-200,
max_val=200))
player = NStepPlayer(BatchedPlayer(env, dqn.online_net), 3)
optimize = dqn.optimize(learning_rate=1e-4)
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(sess, "/root/compo/model.ckpt")
#print('model restored')
replay_buffer = pickle.load(
gzip.open('/root/compo/replay_buffer.p.gz', 'rb'))
replay_buffer.alpha = 0.2
replay_buffer.beta = 0.4
replay_buffer.capacity = 100000
restore_ppo2_weights(sess)
dqn.train(
num_steps=2000000, # Make sure an exception arrives before we stop.
player=player,
replay_buffer=
replay_buffer, #PrioritizedReplayBuffer(500000, 0.5, 0.4, epsilon=0.1),
optimize_op=optimize,
train_interval=4,
target_interval=8192,
batch_size=32,
min_buffer_size=20000)
def main():
"""Run DQN until the environment throws an exception."""
# "results/rainbow/2/videos/6"
env = make_env(stack=False,
scale_rew=False,
render=20,
monitor=None,
timelimit=False)
# env = AllowBacktracking(make_env(stack=False, scale_rew=False))
# TODO we might not want to allow backtracking, it kinda hurts in mario
env = BatchedFrameStack(BatchedGymEnv([[env]]), num_images=4, concat=False)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True # pylint: disable=E1101
config.gpu_options.per_process_gpu_memory_fraction = 0.6
with tf.Session(config=config) as sess:
dqn = DQN(*rainbow_models(sess,
env.action_space.n,
gym_space_vectorizer(env.observation_space),
min_val=-200,
max_val=200))
# TODO rebuild the online_net form the saved model
# type <anyrl.models.dqn_dist.NatureDistQNetwork object at ???>
# important methods
#
model = dqn.online_net
player = NStepPlayer(BatchedPlayer(env, dqn.online_net), 3)
with tf.device("/cpu"):
# sess.run(tf.global_variables_initializer())
vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
try:
for i in tqdm(range(100000)):
trajectories = player.play()
for trajectori in trajectories:
trajectori
pass
except KeyboardInterrupt:
env.close()
def main():
if local_env: # Select Random Level if local
from retro_contest.local import make
levels = [
'SpringYardZone.Act3', 'SpringYardZone.Act2', 'GreenHillZone.Act3',
'GreenHillZone.Act1', 'StarLightZone.Act2', 'StarLightZone.Act1',
'MarbleZone.Act2', 'MarbleZone.Act1', 'MarbleZone.Act3',
'ScrapBrainZone.Act2', 'LabyrinthZone.Act2', 'LabyrinthZone.Act1',
'LabyrinthZone.Act3'
]
level_choice = levels[random.randrange(0, 13, 1)]
env = make(game='SonicTheHedgehog-Genesis', state=level_choice)
else:
print('connecting to remote environment')
env = grc.RemoteEnv('tmp/sock')
print('starting episode')
env = TrackedEnv(env)
solutions = env.solutions # Track Solutions
state_size = env.observation_space
action_size = env.action_space.n
print(state_size, action_size)
env.assist = False
env.trainer = False # Begin with mentor led exploration
env.resume_rl(True) # Begin with RL exploration
env.reset()
while env.total_steps_ever <= TOTAL_TIMESTEPS: # Interact with Retro environment until Total TimeSteps expire.
while env.trainer:
print('Entering Self Play')
keys = getch()
if keys == 'A':
env.control(-1)
if keys == 'B':
env.control(4)
if keys == 'C':
env.control(3)
if keys == 'D':
env.control(2)
if keys == 'rr':
env.trainer = False
continue
if keys == ' ':
env.close()
env = make(game='SonicTheHedgehog-Genesis',
state=levels[random.randrange(0, 13, 1)])
env = TrackedEnv(env)
env.reset() # Initialize Gaming Environment
env.trainer = True
if env.steps > 1:
print('Prev Rew', env.step_rew_history[-1], 'Curr_Loc',
env.reward_history[-1], 'Med Rew',
np.median(env.step_rew_history[-3:]))
if env.episode % RL_PLAY_PCT == 0:
tf.reset_default_graph()
with tf.Session() as sess:
def make_net(name):
return MLPQNetwork(sess,
env.action_space.n,
gym_space_vectorizer(
env.observation_space),
name,
layer_sizes=[32])
dqn = DQN(make_net('online'), make_net('target'))
bplayer = BasicPlayer(env,
EpsGreedyQNetwork(
dqn.online_net, EPSILON),
batch_size=STEPS_PER_UPDATE)
optimize = dqn.optimize(learning_rate=LEARNING_RATE)
sess.run(tf.global_variables_initializer())
env.agent = 'DQN'
dqn.train(
num_steps=TRAINING_STEPS,
player=bplayer,
replay_buffer=PrioritizedReplayBuffer(500000,
0.5,
0.4,
epsilon=0.1),
optimize_op=optimize,
target_interval=200,
batch_size=64,
min_buffer_size=200,
handle_ep=lambda _, rew: print('Exited DQN with : ' + str(
rew) + str(env.steps)))
new_ep = True # New Episode Flag
while new_ep:
if new_ep:
if (solutions and random.random() <
EXPLOIT_BIAS + env.total_steps_ever / TOTAL_TIMESTEPS):
solutions = sorted(solutions, key=lambda x: np.mean(x[0]))
best_pair = solutions[-1]
new_rew = exploit(env, best_pair[1])
best_pair[0].append(new_rew)
print('replayed best with reward %f' % new_rew)
print(best_pair[0])
continue
else:
env.reset()
new_ep = False
env.agent = 'JERK'
rew, new_ep = move(env, 100)
if not new_ep and rew <= 0:
#print('backtracking due to negative reward: %f' % rew)
_, new_ep = move(env, 70, left=True)
if new_ep:
solutions.append(
([max(env.reward_history)], env.best_sequence()))
def main():
"""
Entry-point for the program.
"""
args = _parse_args()
# batched env = creates gym env, not sure what batched means
# make_single_env = GrayscaleEnv > DownsampleEnv
# GrayscaleEnv = turns RGB into grayscale
# DownsampleEnv = down samples observation by N times where N is the specified variable (e.g. 2x smaller)
env = batched_gym_env([partial(make_single_env, args.game)] * args.workers)
env_test = make_single_env(args.game)
#make_single_env(args.game)
print('OBSSSS', env_test.observation_space)
#env = CustomWrapper(args.game)
# Using BatchedFrameStack with concat=False is more
# memory efficient than other stacking options.
env = BatchedFrameStack(env, num_images=4, concat=False)
with tf.Session() as sess:
def make_net(name):
return rainbow_models(sess,
env.action_space.n,
gym_space_vectorizer(env.observation_space),
min_val=-200,
max_val=200)
dqn = DQN(*rainbow_models(sess,
env.action_space.n,
gym_space_vectorizer(env.observation_space),
min_val=-200,
max_val=200))
player = BatchedPlayer(env,
EpsGreedyQNetwork(dqn.online_net, args.epsilon))
optimize = dqn.optimize(learning_rate=args.lr)
sess.run(tf.global_variables_initializer())
reward_hist = []
total_steps = 0
def _handle_ep(steps, rew):
nonlocal total_steps
total_steps += steps
reward_hist.append(rew)
if len(reward_hist) == REWARD_HISTORY:
print('%d steps: mean=%f' %
(total_steps, sum(reward_hist) / len(reward_hist)))
reward_hist.clear()
dqn.train(num_steps=int(1e7),
player=player,
replay_buffer=UniformReplayBuffer(args.buffer_size),
optimize_op=optimize,
target_interval=args.target_interval,
batch_size=args.batch_size,
min_buffer_size=args.min_buffer_size,
handle_ep=_handle_ep)
env.close()
print('creating env')
env = AllowBacktracking(make_env(stack=False, scale_rew=False))
env = BatchedFrameStack(BatchedGymEnv([[env]]), num_images=4, concat=False)
print('creating agent')
online_net, target_net = rainbow_models(sess,
env.action_space.n,
gym_space_vectorizer(
env.observation_space),
min_val=-200,
max_val=200)
dqn = DQN(online_net, target_net)
env.close()
optimize = dqn.optimize(learning_rate=1e-4)
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
train_steps = 1000 #200000
for i in range(3):
stage = random.choice(stages)
game = random.choice(games)
def train(batched_env,
env_count=1,
batch_size_multiplier=32,
num_steps=2000000,
pretrained_model='artifacts/model/model.cpkt',
output_dir='artifacts/model',
use_schedules=True):
"""
Trains on a batched_env using anyrl-py's dqn and rainbow model.
env_count: The number of envs in batched_env
batch_size_multiplier: batch_size of the dqn train call will be env_count * batch_size_multiplier
num_steps: The number of steps to run training for
pretrained_model: Load tf weights from this model file
output_dir: Save tf weights to this file
use_schedules: Enables the tf_schedules for the train call. Schedules require internet access, so don't include on
retro-contest evaluation server
"""
env = CollisionMapWrapper(batched_env)
env = BatchedResizeImageWrapper(env)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True # pylint: disable=E1101
with tf.Session(config=config) as sess:
dqn = DQN(*rainbow_models(sess,
env.action_space.n,
gym_space_vectorizer(env.observation_space),
min_val=-200,
max_val=200))
scheduled_saver = ScheduledSaver(save_interval=10000,
save_dir=output_dir)
print('Outputting trained model to', output_dir)
# Reporting uses BatchedPlayer to get _total_rewards
batched_player = BatchedPlayer(env, dqn.online_net)
player = NStepPlayer(batched_player, 3)
optimize = dqn.optimize(learning_rate=1e-4)
if pretrained_model is None:
print('Initializing with random weights')
sess.run(tf.global_variables_initializer())
else:
print('Loading pre-trained model from', pretrained_model)
scheduled_saver.saver.restore(sess, pretrained_model)
print('Beginning Training, steps', num_steps)
tf_schedules = []
if (use_schedules):
tf_schedules = [
scheduled_saver,
LosswiseSchedule(num_steps, batched_player),
LoadingBar(num_steps)
]
print(env_count * batch_size_multiplier)
dqn.train(
num_steps=num_steps,
player=player,
replay_buffer=PrioritizedReplayBuffer(300000,
0.5,
0.4,
epsilon=0.1),
optimize_op=optimize,
train_interval=env_count,
target_interval=8192,
batch_size=env_count * batch_size_multiplier,
min_buffer_size=max(4500, env_count * batch_size_multiplier),
# min_buffer_size=60,
tf_schedules=tf_schedules,
handle_ep=print)
scheduled_saver.save(sess)
def main():
"""Run DQN until the environment throws an exception."""
config = tf.ConfigProto()
config.gpu_options.allow_growth = True # pylint: disable=E1101
comm = MPI.COMM_WORLD
# Use MPI for parallel evaluation
rank = comm.Get_rank()
size = comm.Get_size()
env_fns, env_names = create_eval_envs()
env = AllowBacktracking(env_fns[rank](stack=False, scale_rew=False))
env = BatchedFrameStack(BatchedGymEnv([[env]]), num_images=4, concat=False)
with tf.Session(config=config) as sess:
dqn = DQN(*rainbow_models(sess,
env.action_space.n,
gym_space_vectorizer(env.observation_space),
min_val=-200,
max_val=200))
player = NStepPlayer(BatchedPlayer(env, dqn.online_net), 3)
optimize = dqn.optimize(learning_rate=1e-4)
sess.run(tf.global_variables_initializer())
reward_hist = []
total_steps = 0
def _handle_ep(steps, rew, env_rewards):
nonlocal total_steps
total_steps += steps
reward_hist.append(rew)
if total_steps % 1 == 0:
avg_score = sum(reward_hist[-100:]) / len(reward_hist[-100:])
# Global Score
global_score = np.zeros(1)
local_score = np.array(avg_score)
print("Local Score for " + env_names[rank] + " at episode " +
str(len(reward_hist)) + " with timesteps: " +
str(total_steps) + ": " + str(local_score))
comm.Allreduce(local_score, global_score, op=MPI.SUM)
global_score /= size
if rank == 0:
print("Global Average Score at episode: " +
str(len(reward_hist)) + ": " + str(global_score))
dqn.train(
num_steps=2000000, # Make sure an exception arrives before we stop.
player=player,
replay_buffer=PrioritizedReplayBuffer(500000,
0.5,
0.4,
epsilon=0.1),
optimize_op=optimize,
train_interval=1,
target_interval=8192,
batch_size=32,
min_buffer_size=20000,
handle_ep=_handle_ep,
save_interval=None,
restore_path=
'./checkpoints_rainbow/model-10' # Model to be evaluated
)
def main():
if local_env: # Select Random Level if local
levels = ['SpringYardZone.Act3',
'SpringYardZone.Act2',
'GreenHillZone.Act3',
'GreenHillZone.Act1',
'StarLightZone.Act2',
'StarLightZone.Act1',
'MarbleZone.Act2',
'MarbleZone.Act1',
'MarbleZone.Act3',
'ScrapBrainZone.Act2',
'LabyrinthZone.Act2',
'LabyrinthZone.Act1',
'LabyrinthZone.Act3']
level_choice = random.randrange(0, 13, 1)
env = make_env(stack=True, scale_rew=False, local=local_env, level_choice=level_choice) #-3
else:
print('connecting to remote environment')
env = grc.RemoteEnv('tmp/sock')
print('starting episode')
env = AllowBacktracking(env)
solutions = env.solutions # Track Solutions
state_size = env.observation_space
action_size = env.action_space.n
print(state_size, action_size)
env.assist = False
env.trainer = train # Begin with mentor led exploration
env.reset()
while env.total_steps_ever <= TOTAL_TIMESTEPS: # Interact with Retro environment until Total TimeSteps expire.
while env.trainer:
print('Entering Self Play')
keys = getch()
if keys == 'A':
env.control(-1)
if keys == 'B':
env.control(4)
if keys == 'C':
env.control(3)
if keys == 'D':
env.control(2)
buttons = ["B", "A", "MODE", "START", "UP", "DOWN", "LEFT", "RIGHT", "C", "Y", "X", "Z"]
actions = [['LEFT'], ['RIGHT'], ['LEFT', 'DOWN'], ['RIGHT', 'DOWN'], ['DOWN'],
['DOWN', 'B'], ['B']]
if keys == 'rr':
env.trainer = False
continue
if keys == ' ':
env.close()
env = make_env(stack=False, scale_rew=False, local=local_env)
env = AllowBacktracking(env)
env.reset() # Initialize Gaming Environment
env.trainer = True
if env.episode % RL_PLAY_PCT == 0:
tf.reset_default_graph()
with tf.Session() as sess:
def make_net(name):
return MLPQNetwork(sess,
env.action_space.n,
gym_space_vectorizer(env.observation_space),
name,
layer_sizes=[32])
dqn = DQN(make_net('online'), make_net('target'))
bplayer = BasicPlayer(env, EpsGreedyQNetwork(dqn.online_net, EPSILON),
batch_size=STEPS_PER_UPDATE)
optimize = dqn.optimize(learning_rate=LEARNING_RATE)
sess.run(tf.global_variables_initializer())
env.agent = 'DQN'
dqn.train(num_steps=TRAINING_STEPS,
player=bplayer,
replay_buffer=PrioritizedReplayBuffer(500000, 0.5, 0.4, epsilon=0.1),
optimize_op=optimize,
target_interval=200,
batch_size=64,
min_buffer_size=200,
handle_ep=lambda _, rew: print('Exited DQN with : ' + str(rew) + str(env.steps)))
new_ep = True # New Episode Flag
while new_ep:
if new_ep:
if (solutions and
random.random() < EXPLOIT_BIAS + env.total_steps_ever / TOTAL_TIMESTEPS):
new_state, new_rew, done = env.spawn()
continue
else:
env.reset()
new_ep = False
env.agent = 'JERK'
rew, new_ep = move(env, 100)
if not new_ep and rew <= 0:
#print('backtracking due to negative reward: %f' % rew)
_, new_ep = move(env, 70, left=True)
if new_ep:
solutions.append(([max(env.reward_history)], env.best_sequence()))
def main():
"""Run DQN until the environment throws an exception."""
#env = AllowBacktracking(make_env(stack=False, scale_rew=False))
#envs = make_training_envs()
#env = BatchedFrameStack(BatchedGymEnv(envs), num_images=4, concat=False)
#env = BatchedFrameStack(BatchedGymEnv([[env]]), num_images=4, concat=False)
envs = get_training_envs()
game, state = random.choice(envs)
env = make_training_env(game, state, stack=False, scale_rew=False)
env = prep_env(env)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True # pylint: disable=E1101
with tf.Session(config=config) as sess:
dqn = DQN(*models(sess,
env.action_space.n,
gym_space_vectorizer(env.observation_space),
min_val=-200,
max_val=200))
player = NStepPlayer(BatchedPlayer(env, dqn.online_net), 3)
optimize = dqn.optimize(learning_rate=1e-4)
loss = dqn.loss
train_writer = tf.summary.FileWriter('./logs/multiple/train',
sess.graph)
tf.summary.scalar("loss", loss)
reward = tf.Variable(0., name='reward', trainable=False)
tf.summary.scalar('reward', tf.reduce_mean(reward))
steps = tf.Variable(0, name='steps', trainable=False)
tf.summary.scalar('steps', tf.reduce_mean(steps))
summary_op = tf.summary.merge_all()
sess.run(tf.global_variables_initializer())
print(tf.trainable_variables())
#graph = tf.get_default_graph()
#restore_saver = tf.train.Saver({
# 'dense1/bias': graph.get_tensor_by_name('online/dense1/bias:0'),
# 'dense1/kernel': graph.get_tensor_by_name('online/dense1/kernel:0'),
# 'layer_1/bias': graph.get_tensor_by_name('online/layer_1/bias:0'),
# 'layer_1/kernel': graph.get_tensor_by_name('online/layer_1/kernel:0'),
# 'layer_2/bias': graph.get_tensor_by_name('online/layer_2/bias:0'),
# 'layer_2/kernel': graph.get_tensor_by_name('online/layer_2/kernel:0'),
# 'layer_3/bias': graph.get_tensor_by_name('online/layer_3/bias:0'),
# 'layer_3/kernel': graph.get_tensor_by_name('online/layer_3/kernel:0'),
# 'dense1/bias': graph.get_tensor_by_name('online_1/dense1/bias:0'),
# 'dense1/kernel': graph.get_tensor_by_name('online_1/dense1/kernel:0'),
# 'layer_1/bias': graph.get_tensor_by_name('online_1/layer_1/bias:0'),
# 'layer_1/kernel': graph.get_tensor_by_name('online_1/layer_1/kernel:0'),
# 'layer_2/bias': graph.get_tensor_by_name('online_1/layer_2/bias:0'),
# 'layer_2/kernel': graph.get_tensor_by_name('online_1/layer_2/kernel:0'),
# 'layer_3/bias': graph.get_tensor_by_name('online_1/layer_3/bias:0'),
# 'layer_3/kernel': graph.get_tensor_by_name('online_1/layer_3/kernel:0'),
# 'dense1/bias': graph.get_tensor_by_name('online_2/dense1/bias:0'),
# 'dense1/kernel': graph.get_tensor_by_name('online_2/dense1/kernel:0'),
# 'layer_1/bias': graph.get_tensor_by_name('online_2/layer_1/bias:0'),
# 'layer_1/kernel': graph.get_tensor_by_name('online_2/layer_1/kernel:0'),
# 'layer_2/bias': graph.get_tensor_by_name('online_2/layer_2/bias:0'),
# 'layer_2/kernel': graph.get_tensor_by_name('online_2/layer_2/kernel:0'),
# 'layer_3/bias': graph.get_tensor_by_name('online_2/layer_3/bias:0'),
# 'layer_3/kernel': graph.get_tensor_by_name('online_2/layer_3/kernel:0'),
# 'dense1/bias': graph.get_tensor_by_name('target/dense1/bias:0'),
# 'dense1/kernel': graph.get_tensor_by_name('target/dense1/kernel:0'),
# 'layer_1/bias': graph.get_tensor_by_name('target/layer_1/bias:0'),
# 'layer_1/kernel': graph.get_tensor_by_name('target/layer_1/kernel:0'),
# 'layer_2/bias': graph.get_tensor_by_name('target/layer_2/bias:0'),
# 'layer_2/kernel': graph.get_tensor_by_name('target/layer_2/kernel:0'),
# 'layer_3/bias': graph.get_tensor_by_name('target/layer_3/bias:0'),
# 'layer_3/kernel': graph.get_tensor_by_name('target/layer_3/kernel:0'),
# })
#restore_saver.restore(sess, './model-images/model.ckpt')
#print('model restored')
weights = joblib.load('./ppo2_weights_266.joblib')
#[<tf.Variable 'model/c1/w:0' shape=(8, 8, 4, 32) dtype=float32_ref>, <tf.Variable 'model/c1/b:0' shape=(1, 32, 1, 1) dtype=float32_ref>, <tf.Variable 'model/c2/w:0' shape=(4, 4, 32, 64) dtype=float32_ref>, <tf.Variable 'model/c2/b:0' shape=(1, 64, 1, 1) dtype=float32_ref>, <tf.Variable 'model/c3/w:0' shape=(3, 3, 64, 64) dtype=float32_ref>, <tf.Variable 'model/c3/b:0' shape=(1, 64, 1, 1) dtype=float32_ref>, <tf.Variable 'model/fc1/w:0' shape=(3136, 512) dtype=float32_ref>, <tf.Variable 'model/fc1/b:0' shape=(512,) dtype=float32_ref>, <tf.Variable 'model/v/w:0' shape=(512, 1) dtype=float32_ref>, <tf.Variable 'model/v/b:0' shape=(1,) dtype=float32_ref>, <tf.Variable 'model/pi/w:0' shape=(512, 7) dtype=float32_ref>, <tf.Variable 'model/pi/b:0' shape=(7,) dtype=float32_ref>]
graph = tf.get_default_graph()
for model in ['online', 'target']:
tensor_names = [
'{}/layer_1/conv2d/kernel:0',
'{}/layer_1/conv2d/bias:0',
'{}/layer_2/conv2d/kernel:0',
'{}/layer_2/conv2d/bias:0',
'{}/layer_3/conv2d/kernel:0',
'{}/layer_3/conv2d/bias:0',
#'{}/dense1/kernel:0',
#'{}/dense1/bias:0'
]
for i in range(len(tensor_names)):
tensor_name = tensor_names[i].format(model)
tensor = graph.get_tensor_by_name(tensor_name)
weight = weights[i]
if 'bias' in tensor_name:
weight = np.reshape(weight, tensor.get_shape())
print('about to assign {} value with size {}'.format(
tensor_name, weights[i].shape))
sess.run(tf.assign(tensor, weight))
saver = tf.train.Saver()
save_path = saver.save(sess, "./model/model.ckpt")
print('Saved model')
replay_buffer = PrioritizedReplayBuffer(100000, 0.5, 0.4, epsilon=0.1)
#replay_buffer = pickle.load(gzip.open('./docker-build/model/replay_buffer.p.gz', 'rb'))
#replay_buffer = pickle.load(open('./model/replay_buffer.p', 'rb'))
total_steps = 50000000
steps_per_env = 5000
env.close()
for i in range(int(total_steps / steps_per_env)):
game, state = random.choice(envs)
env = make_training_env(game, state, stack=False, scale_rew=False)
env = prep_env(env)
player = NStepPlayer(BatchedPlayer(env, dqn.online_net), 3)
#dqn.train(num_steps=steps_per_env, # Make sure an exception arrives before we stop.
# player=player,
# replay_buffer=replay_buffer,
# optimize_op=optimize,
# train_interval=1,
# target_interval=8192,
# batch_size=32,
# min_buffer_size=20000)
summary = train(
dqn,
num_steps=
steps_per_env, # Make sure an exception arrives before we stop.
player=player,
replay_buffer=replay_buffer,
optimize_op=optimize,
train_interval=4,
target_interval=8192,
batch_size=32,
min_buffer_size=20000,
summary_op=summary_op,
handle_ep=lambda st, rew:
(reward.assign(rew), steps.assign(st)),
handle_step=lambda st, rew:
(reward.assign(reward + rew), steps.assign(steps + st)))
env.close()
if summary:
train_writer.add_summary(summary, i)
else:
print('No summary')
save_path = saver.save(sess, "./model/model.ckpt")
pickle.dump(replay_buffer, open("./model/replay_buffer.p", "wb"))
print('Saved model')
def test_remote(args):
if not args.anyrl:
agent_folder = args.folder
env = EnvWrap(RemoteEnv(os.environ['REMOTE_HOST']), remote_testing=True)
agent = load_agent(agent_folder, save_dir="./{}/".format(agent_folder))
test(agent, env)
agent.close()
env.close()
else:
online = builder.build_network(sess, 'online')
target = builder.build_network(sess, 'target')
dqn = DQN(online, target)
sess.run(tf.global_variables_initializer())
with tf.Session(graph=tf.Graph()) as sess:
sess.run(tf.global_variables_initializer())
with tf.Session(graph=tf.Graph()) as sess:
def add_hyperopt_parser(subparsers):
parser_hyperopt = subparsers.add_parser('hyperopt')
parser_hyperopt.add_argument("environment", type=str,
help="OpenAI Gym environment to train the agent on")
parser_hyperopt.add_argument("num_agents", type=int,
help="Number of agents to be generated and tested")
parser_hyperopt.add_argument("num_episodes", type=int,
help="Number of episodes each agent should be trained on")
parser_hyperopt.add_argument('-b', '--base-agent', type=str,
help="JSON file to load the base agent spec from", default="agent.json")
parser_hyperopt.add_argument('-hp', '--hyperparam-space', type=str,
help="JSON file to load the hyperparameter search space from",
default="hyperparam_space.json")
def add_train_parser(subparsers):
parser_train = subparsers.add_parser('train')
parser_train.add_argument("agent_name", type=str,
help="Name of the folder the agent is stored in \
within the folder specified by --folder (-f)")
parser_train.add_argument("num_episodes", type=int,
help="Number of episodes to train on")
def add_test_remote_parser(subparsers):
parser_test_remote = subparsers.add_parser('test-remote')
parser_test_remote.add_argument("agent_name", type=str,
help="Name of the folder the agent is stored in \
within the folder specified by --folder (-f)")
def parse_args():
parser = argparse.ArgumentParser(
description="Suite of tools for training reinforcement learning agents")
subparsers = parser.add_subparsers()
subparsers.required = True
subparsers.dest = 'mode'
parser.add_argument('-f', '--folder', type=str,
help="Folder the agent files are stored in (default: ./models)", default="./models")
parser.add_argument('-d', '--anyrl', action="store_true", help="Use DQN agent")
parser.add_argument('-r', '--rainbow', action="store_true",
help="Use rainbow DQN agent")
parser.add_argument('-s', '--seasonals', action="store_true",
help="Use seasonals environment")
add_hyperopt_parser(subparsers)
add_train_parser(subparsers)
add_test_remote_parser(subparsers)
return parser.parse_args()
def main():
args = parse_args()
if args.environment == "seasonals-v1":
import gym
from gym_seasonals.envs.seasonals_env import SeasonalsEnv
from gym_seasonals.envs.seasonals_env import portfolio_var
from gym_seasonals.envs.remote_env import RemoteEnv
if args.mode == "hyperopt":
hyperparam_search(space_filename=args.hyperparam_space, env_name=args.environment,
num_tests=args.num_agents, num_episodes=args.num_episodes,
save_folder=args.folder, base_agent=args.base_agent,
anyrl=args.anyrl,
rainbow=args.rainbow)
elif args.mode == 'train':
dqn_train(args)
elif args.mode == 'test-remote':
test_remote(args)
def anyrl_experiment(args, env_name, agent_folder=None, visualize=True,
num_episodes=500, rainbow=False, seasonals=False):
tf.reset_default_graph()
seasonals = env_name == 'seasonals-v1'
if seasonals:
os.mkdir(agent_folder)
ep_record = Record(filename=os.path.join(agent_folder, "model"))
handle_ep = functools.partial(handle_ep_with_context, num_episodes, ep_record)
def video_callable_with_context(context, episode_id):
if episode_id == 50 or episode_id % 100 == 0:
return True
if np.mean(context.rewards[-98:]) > 200:
return True
return False
env = gym.wrappers.Monitor(
gym.make(env_name),
os.path.join(agent_folder, "monitor"),
video_callable=functools.partial(
video_callable_with_context, ep_record
)
) if not seasonals else EnvWrap(
gym.make('seasonals-v1'))
env.set_simulation_window(start_index=1500, end_index=1968)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
builder_class = RainbowDQNBuilder if rainbow else DQNBuilder
builder = builder_class(env, args)
with tf.Session(config=config) as sess:
online = builder.build_network(sess, 'online')
target = builder.build_network(sess, 'target')
dqn = DQN(online, target)
train_kwargs = builder.finish(sess, dqn)
sess.run(tf.global_variables_initializer())
try:
dqn.train(num_steps=3000000,
train_interval=4,
target_interval=args['target_sync_frequency'],
batch_size=32,
min_buffer_size=200,
handle_ep=handle_ep,
**train_kwargs)
except gym.error.Error:
aves, std_dev = plot_rewards(ep_record.rewards,
save_dir=agent_folder, ylims=((-500, 250)
if not seasonals else None),
half_window=round(num_episodes / 20))
pass
except KeyboardInterrupt:
aves, std_dev = plot_rewards(ep_record.rewards,
save_dir=agent_folder, ylims=((-500, 250)
if not seasonals else None),
half_window=round(num_episodes / 20))
raise
filename = os.path.join(agent_folder, "model")
if not os.path.exists(filename):
os.mkdir(filename)
tf.train.Saver().save(sess, os.path.join(filename, "model-best.ckpt"))
experiment_data = {"final_test_reward":np.mean(ep_record.rewards[-1]),
"test_average_last_50":np.mean(ep_record.rewards[-50::10]),
"train_average_last_50":np.mean(ep_record.rewards[-50:]),
"test_average_last_10":np.mean(ep_record.rewards[-10::5]),
"train_average_last_10":np.mean(ep_record.rewards[-10:]),
"max_score":max(ep_record.rewards)
}
experiment_data.update(args)
experiment_data['solved'] = True \
if np.mean(ep_record.rewards[-100:]) > 200 \
else False
experiment_data['final_std_dev'] = std_dev
del builder
del dqn
env.close()
experiment_data['agent'] = agent_folder.split('/')[-1]
return experiment_data
def main():
"""Run DQN until the environment throws an exception."""
print('creating env')
env = AllowBacktracking(make_env(stack=False, scale_rew=False))
env = BatchedFrameStack(BatchedGymEnv([[env]]), num_images=4, concat=False)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True # pylint: disable=E1101
print('starting tf session')
with tf.Session(config=config) as sess:
print('creating agent')
online_net, target_net = rainbow_models(sess,
env.action_space.n,
gym_space_vectorizer(
env.observation_space),
min_val=-200,
max_val=200)
dqn = DQN(online_net, target_net)
player = NStepPlayer(BatchedPlayer(env, dqn.online_net), 3)
optimize = dqn.optimize(learning_rate=1e-4)
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
train_steps = 5000
print('training steps:', train_steps)
for j in range(1):
print(j)
start = time.time()
dqn.train(
num_steps=
train_steps, # Make sure an exception arrives before we stop.
player=player,
replay_buffer=PrioritizedReplayBuffer(500000,
0.5,
0.4,
epsilon=0.1),
optimize_op=optimize,
train_interval=1,
target_interval=8192,
batch_size=32,
min_buffer_size=10000)
end = time.time()
print(end - start)
print('done training')
print('save nn')
save_path = saver.save(sess, "saved_models/rainbow5.ckpt")
print("Model saved in path: %s" % save_path)
tvars = tf.trainable_variables()
tvars_vals = sess.run(tvars)
#for var, val in zip(tvars, tvars_vals):
# print(var.name, val[0])
#print(tvars_vals[0][-5:])
#print('stepping')
#obs = env.reset()
#online_net.step(obs, obs)
'''
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--restore',
'-restore',
action='store_true',
help='restore from checkpoint file')
parser.add_argument('--record',
'-record',
action='store_true',
help='record bk2 movies')
args = parser.parse_args()
"""Run DQN until the environment throws an exception."""
env = AllowBacktracking(
make_env(stack=False, scale_rew=False, record=args.record))
env = BatchedFrameStack(BatchedGymEnv([[env]]), num_images=4, concat=False)
checkpoint_dir = os.path.join(os.getcwd(), 'results')
results_dir = os.path.join(os.getcwd(), 'results',
time.strftime("%d-%m-%Y_%H-%M-%S"))
if not os.path.exists(results_dir):
os.makedirs(results_dir)
summary_writer = tf.summary.FileWriter(results_dir)
# TODO
# env = wrappers.Monitor(env, results_dir, force=True)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True # pylint: disable=E1101
with tf.Session(config=config) as sess:
dqn = DQN(*rainbow_models(sess,
env.action_space.n,
gym_space_vectorizer(env.observation_space),
min_val=-200,
max_val=200))
saver = tf.train.Saver()
if args.restore:
latest_checkpoint = tf.train.latest_checkpoint(checkpoint_dir)
if latest_checkpoint:
print("Loading model checkpoint {} ...\n".format(
latest_checkpoint))
saver.restore(sess, latest_checkpoint)
else:
print("Checkpoint not found")
player = NStepPlayer(BatchedPlayer(env, dqn.online_net), 3)
optimize = dqn.optimize(learning_rate=1e-4)
sess.run(tf.global_variables_initializer())
reward_hist = []
total_steps = 0
# runs with every completed episode
def _handle_ep(steps, rew):
nonlocal total_steps
total_steps += steps
reward_hist.append(rew)
summary_reward = tf.Summary()
summary_reward.value.add(tag='global/reward', simple_value=rew)
summary_writer.add_summary(summary_reward, global_step=total_steps)
print('save model')
saver.save(sess=sess,
save_path=checkpoint_dir + '/model',
global_step=total_steps)
if len(reward_hist) == REWARD_HISTORY:
print('%d steps: mean=%f' %
(total_steps, sum(reward_hist) / len(reward_hist)))
summary_meanreward = tf.Summary()
summary_meanreward.value.add(tag='global/mean_reward',
simple_value=sum(reward_hist) /
len(reward_hist))
summary_writer.add_summary(summary_meanreward,
global_step=total_steps)
reward_hist.clear()
dqn.train(
num_steps=7000000, # Make sure an exception arrives before we stop.
player=player,
replay_buffer=PrioritizedReplayBuffer(500000,
0.5,
0.4,
epsilon=0.1),
optimize_op=optimize,
train_interval=1,
target_interval=8192,
batch_size=32,
min_buffer_size=20000,
handle_ep=_handle_ep)
def main():
discount = os.environ.get('RETRO_DISCOUNT')
if discount != None:
discount = float(discount)
else:
discount = 0.99
print("DISCOUNT: %s" % (discount, ))
"""Run DQN until the environment throws an exception."""
config = tf.ConfigProto()
config.gpu_options.allow_growth = True # pylint: disable=E1101
config.log_device_placement = False
with tf.Session(config=config) as sess:
state_encoder = StateEncoder(sess)
env = make_batched_env()
env_ids = env.env_ids
env = BatchedFrameStack(env, num_images=4, concat=True)
env.env_ids = env_ids
env = ExplorationBatchedEnv(env,
Exploration,
state_encoder=state_encoder)
if 'RETRO_POLICY_DIR' in os.environ:
expert = PolicyExpert(sess,
batch_size=1,
policy_dir=os.environ['RETRO_POLICY_DIR'])
elif not 'RETRO_NOEXPERT' in os.environ:
expert = RandomMoveExpert()
else:
expert = None
if os.environ['RETRO_DQN'] == 'soft_noisy_net':
dqn = DQN(*soft_noisy_net_models(
sess,
env.action_space.n,
gym_space_vectorizer(env.observation_space),
discount=discount, #0.99
expert=expert))
elif os.environ['RETRO_DQN'] == 'soft_rainbow':
dqn = DQN(*soft_rainbow_models(
sess,
env.action_space.n,
gym_space_vectorizer(env.observation_space),
num_atoms=101,
min_val=-1000, #-200
max_val=1000, #200
discount=discount, #0.99
expert=expert))
if "RETRO_CHECKPOINT_DIR" in os.environ:
scheduler_saver = ScheduledSaver(
sess, os.environ["RETRO_CHECKPOINT_DIR"] + "/tensorflow/")
else:
scheduler_saver = None
player = NStepPlayer(BatchedPlayer(env, dqn.online_net), 3)
optimize = dqn.optimize(learning_rate=1e-4)
sess.run(tf.global_variables_initializer())
if 'RETRO_INIT_DIR' in os.environ:
saver = tf.train.Saver(var_list=list(
filter(
lambda v: not 'sigma' in v.name and
not 'dqn_model/noisy_layer_1' in v.name and
not 'dqn_model/noisy_layer_2' in v.name,
tf.trainable_variables('^dqn_model/'))))
latest_checkpoint = tf.train.latest_checkpoint(
os.environ['RETRO_INIT_DIR'])
print("DQN_INIT_CHECKPOINT: %s" % (latest_checkpoint, ))
saver.restore(sess, latest_checkpoint)
#from tensorflow.python.tools import inspect_checkpoint as chkp
#chkp.print_tensors_in_checkpoint_file(latest_checkpoint,'',all_tensors=True)
state_encoder.initialize()
if expert:
expert.initialize()
replay_buffer = PrioritizedReplayBuffer(int(
os.environ.get("RETRO_DQN_BUFFER_SIZE", 250000)),
0.5,
0.4,
epsilon=0.1)
dqn.train(
num_steps=1000000, # Make sure an exception arrives before we stop.
player=player,
replay_buffer=replay_buffer,
optimize_op=optimize,
train_interval=1,
target_interval=int(
os.environ.get("RETRO_DQN_TARGET_INTERVAL", 8192)),
batch_size=32,
min_buffer_size=int(
os.environ.get('RETRO_DQN_MIN_BUFFER_SIZE', 20000)),
handle_ep=lambda steps, rew: scheduler_saver.handle_episode(steps)
if scheduler_saver is not None else None)
