def __init__(self, n_particles, game, game_params, gamma):
self.gamma = gamma
self.envs = []
self.states = []
for _ in n_particles:
env = make_game(game, game_params)
env.seed(np.random.randint(1e7)) # draw some Env seed
s = env.reset()
self.envs.append(env)
self.states.append(s)
def train(game, n_ep, n_mcts, max_ep_len, lr, c, gamma, data_size, batch_size,
temp, n_hidden_layers, n_hidden_units):
""" Outer training loop """
episode_returns = [] # storage
timepoints = []
# Environments
if game == "teaching":
env = TeachingEnv()
bootstrap_last_state_value = False
else:
env = make_game(game)
bootstrap_last_state_value = True
is_atari = is_atari_game(env)
mcts_env = make_game(game) if is_atari else None
replay_buffer = ReplayBuffer(max_size=data_size, batch_size=batch_size)
model = Model(env=env,
lr=lr,
n_hidden_layers=n_hidden_layers,
n_hidden_units=n_hidden_units)
t_total = 0 # total steps
R_best = -np.Inf
start_training = time.time()
for ep in range(n_ep):
start = time.time()
s = env.reset()
R = 0.0 # Total return counter
a_store = []
seed = np.random.randint(1e7) # draw some Env seed
env.seed(seed)
if is_atari:
mcts_env.reset()
mcts_env.seed(seed)
# the object responsible for MCTS searches
mcts = MCTS(root_index=s,
root=None,
model=model,
na=model.action_dim,
gamma=gamma,
bootstrap_last_state_value=bootstrap_last_state_value)
if game == "teaching":
iterator = tqdm(range(env.t_max))
else:
iterator = range(max_ep_len)
for _ in iterator:
# MCTS step
mcts.search(n_mcts=n_mcts, c=c, env=env,
mcts_env=mcts_env) # perform a forward search
state, pi, v = mcts.return_results(temp) # extract the root output
replay_buffer.store((state, v, pi))
# Make the true step
a = np.random.choice(len(pi), p=pi)
a_store.append(a)
s1, r, terminal, _ = env.step(a)
R += r
t_total += n_mcts # total number of environment steps (counts the mcts steps)
if terminal:
break
else:
mcts.forward(a, s1)
# Finished episode
episode_returns.append(R) # store the total episode return
timepoints.append(
t_total) # store the timestep count of the episode return
store_safely({'R': episode_returns, 't': timepoints})
if R > R_best:
a_best = a_store
seed_best = seed
R_best = R
print(f'Finished episode {ep}, total return: {np.round(R,2)}, '
f'time episode: {time.time()-start:.1f} sec, '
f'total time since: {time.time()-start_training:.1f} sec')
# Train
replay_buffer.shuffle()
for sb, vb, pib in replay_buffer:
model.train_on_example(sb=sb, vb=vb, pib=pib)
# Return results
return episode_returns, timepoints, a_best, seed_best, R_best
def agent(game,
n_ep,
n_mcts,
max_ep_len,
lr,
c,
gamma,
data_size,
batch_size,
temp,
n_hidden_layers,
n_hidden_units,
stochastic=False,
eval_freq=-1,
eval_episodes=100,
alpha=0.6,
out_dir='../',
pre_process=None,
visualize=False):
''' Outer training loop '''
if pre_process is not None:
pre_process()
# tf.reset_default_graph()
if not os.path.exists(out_dir):
os.makedirs(out_dir)
episode_returns = [] # storage
timepoints = []
# Environments
Env = make_game(game)
is_atari = is_atari_game(Env)
mcts_env = make_game(game) if is_atari else None
online_scores = []
offline_scores = []
mcts_params = dict(gamma=gamma)
if stochastic:
mcts_params['alpha'] = alpha
mcts_maker = MCTSStochastic
else:
mcts_maker = MCTS
D = Database(max_size=data_size, batch_size=batch_size)
model = Model(Env=Env,
lr=lr,
n_hidden_layers=n_hidden_layers,
n_hidden_units=n_hidden_units)
t_total = 0 # total steps
R_best = -np.Inf
with tf.Session() as sess:
model.sess = sess
sess.run(tf.global_variables_initializer())
for ep in range(n_ep):
if eval_freq > 0 and ep % eval_freq == 0: #and ep > 0
print(
'Evaluating policy for {} episodes!'.format(eval_episodes))
seed = np.random.randint(1e7) # draw some Env seed
Env.seed(seed)
s = Env.reset()
mcts = mcts_maker(root_index=s,
root=None,
model=model,
na=model.action_dim,
**mcts_params)
env_wrapper = EnvEvalWrapper()
env_wrapper.mcts = mcts
starting_states = []
def reset_env():
s = Env.reset()
env_wrapper.mcts = mcts_maker(root_index=s,
root=None,
model=model,
na=model.action_dim,
**mcts_params)
starting_states.append(s)
if env_wrapper.curr_probs is not None:
env_wrapper.episode_probabilities.append(
env_wrapper.curr_probs)
env_wrapper.curr_probs = []
return s
def forward(a, s, r):
env_wrapper.mcts.forward(a, s, r)
#pass
env_wrapper.reset = reset_env
env_wrapper.step = lambda x: Env.step(x)
env_wrapper.forward = forward
env_wrapper.episode_probabilities = []
env_wrapper.curr_probs = None
def pi_wrapper(ob):
if not is_atari:
mcts_env = None
env_wrapper.mcts.search(n_mcts=n_mcts,
c=c,
Env=Env,
mcts_env=mcts_env)
state, pi, V = env_wrapper.mcts.return_results(temp=0)
#pi = model.predict_pi(s).flatten()
env_wrapper.curr_probs.append(pi)
a = np.argmax(pi)
return a
rews, lens = eval_policy(pi_wrapper,
env_wrapper,
n_episodes=eval_episodes,
verbose=True)
offline_scores.append([
np.min(rews),
np.max(rews),
np.mean(rews),
np.std(rews),
len(rews),
np.mean(lens)
])
# if len(rews) < eval_episodes or len(rews) == 0:
# print("WTF")
# if np.std(rews) == 0.:
# print("WTF 2")
np.save(out_dir + '/offline_scores.npy', offline_scores)
start = time.time()
s = Env.reset()
R = 0.0 # Total return counter
a_store = []
seed = np.random.randint(1e7) # draw some Env seed
Env.seed(seed)
if is_atari:
mcts_env.reset()
mcts_env.seed(seed)
if ep % eval_freq == 0:
print("Collecting %d episodes" % eval_freq)
mcts = mcts_maker(
root_index=s,
root=None,
model=model,
na=model.action_dim,
**mcts_params) # the object responsible for MCTS searches
for t in range(max_ep_len):
# MCTS step
if not is_atari:
mcts_env = None
mcts.search(n_mcts=n_mcts, c=c, Env=Env,
mcts_env=mcts_env) # perform a forward search
if visualize:
mcts.visualize()
state, pi, V = mcts.return_results(
temp) # extract the root output
D.store((state, V, pi))
# Make the true step
a = np.random.choice(len(pi), p=pi)
a_store.append(a)
s1, r, terminal, _ = Env.step(a)
R += r
t_total += n_mcts # total number of environment steps (counts the mcts steps)
if terminal:
break
else:
mcts.forward(a, s1, r)
# Finished episode
episode_returns.append(R) # store the total episode return
online_scores.append(R)
timepoints.append(
t_total) # store the timestep count of the episode return
store_safely(out_dir, 'result', {
'R': episode_returns,
't': timepoints
})
np.save(out_dir + '/online_scores.npy', online_scores)
# print('Finished episode {}, total return: {}, total time: {} sec'.format(ep, np.round(R, 2),
# np.round((time.time() - start),
# 1)))
if R > R_best:
a_best = a_store
seed_best = seed
R_best = R
# Train
D.reshuffle()
try:
for epoch in range(1):
for sb, Vb, pib in D:
model.train(sb, Vb, pib)
except Exception as e:
print("ASD")
model.save(out_dir + 'model')
# Return results
return episode_returns, timepoints, a_best, seed_best, R_best, offline_scores
if not args.gpu:
os.environ["CUDA_VISIBLE_DEVICES"] = ""
# Accept custom grid if the environment requires it
if args.game == 'Taxi' or args.game == 'TaxiEasy':
game_params['grid'] = args.grid
game_params['box'] = True
if args.game == 'RaceStrategy-v0':
game_params['horizon'] = args.horizon
basedir = args.logdir
ts = str(time.time())
logdir = basedir + args.game + '/' + ts + '/'
if not os.path.exists(logdir):
os.makedirs(logdir)
env = make_game(args.game, game_params)
state_dim = env.observation_space.shape[0]
discrete = True
try:
action_dim = env.action_space.n
except:
action_dim = env.action_space.high.shape[0]
discrete = False
pi = load_policy(model_path='',
input_dim=state_dim,
output_dim=action_dim,
num_hidden=args.n_hidden_units,
num_layers=args.n_hidden_layers,
discrete=discrete,
beta=1.0)
def agent(game, n_ep, n_mcts, max_ep_len, lr, c, gamma, data_size, batch_size,
temp, n_hidden_layers, n_hidden_units):
''' Outer training loop '''
# tf.reset_default_graph()
episode_returns = [] # storage
timepoints = []
# Environments
Env = make_game(game)
is_atari = is_atari_game(Env)
mcts_env = make_game(game) if is_atari else None
D = Database(max_size=data_size, batch_size=batch_size)
model = Model(Env=Env,
lr=lr,
n_hidden_layers=n_hidden_layers,
n_hidden_units=n_hidden_units)
t_total = 0 # total steps
R_best = -np.Inf
with tf.Session() as sess:
model.sess = sess
sess.run(tf.global_variables_initializer())
for ep in range(n_ep):
start = time.time()
s = Env.reset()
R = 0.0 # Total return counter
a_store = []
seed = np.random.randint(1e7) # draw some Env seed
Env.seed(seed)
if is_atari:
mcts_env.reset()
mcts_env.seed(seed)
mcts = MCTS(
root_index=s,
root=None,
model=model,
na=model.action_dim,
gamma=gamma) # the object responsible for MCTS searches
for t in range(max_ep_len):
# MCTS step
mcts.search(n_mcts=n_mcts, c=c, Env=Env,
mcts_env=mcts_env) # perform a forward search
state, pi, V = mcts.return_results(
temp) # extract the root output
D.store((state, V, pi))
# Make the true step
a = np.random.choice(len(pi), p=pi)
a_store.append(a)
s1, r, terminal, _ = Env.step(a)
R += r
t_total += n_mcts # total number of environment steps (counts the mcts steps)
if terminal:
break
else:
mcts.forward(a, s1)
# Finished episode
episode_returns.append(R) # store the total episode return
timepoints.append(
t_total) # store the timestep count of the episode return
store_safely(os.getcwd(), 'result', {
'R': episode_returns,
't': timepoints
})
if R > R_best:
a_best = a_store
seed_best = seed
R_best = R
print('Finished episode {}, total return: {}, total time: {} sec'.
format(ep, np.round(R, 2), np.round((time.time() - start),
1)))
# Train
D.reshuffle()
for epoch in range(1):
for sb, Vb, pib in D:
model.train(sb, Vb, pib)
# Return results
return episode_returns, timepoints, a_best, seed_best, R_best
def agent(game,
n_ep,
n_mcts,
max_ep_len,
lr,
c,
gamma,
data_size,
batch_size,
temp,
n_hidden_layers,
n_hidden_units,
stochastic=False,
eval_freq=-1,
eval_episodes=100,
alpha=0.6,
n_epochs=100,
c_dpw=1,
numpy_dump_dir='../',
pre_process=None,
visualize=False,
game_params={},
parallelize_evaluation=False,
mcts_only=False,
particles=0,
show_plots=False,
n_workers=1,
use_sampler=False,
budget=np.inf,
unbiased=False,
biased=False,
max_workers=100,
variance=False,
depth_based_bias=False,
scheduler_params=None,
out_dir=None,
render=False,
second_version=False,
third_version=False):
visualizer = None
# if particles:
# parallelize_evaluation = False # Cannot run parallelized evaluation with particle filtering
if not mcts_only:
from mcts import MCTS
from mcts_dpw import MCTSStochastic
elif particles:
if unbiased:
from particle_filtering.ol_uct import OL_MCTS
elif biased:
if second_version:
from particle_filtering.pf_uct_2 import PFMCTS2 as PFMCTS
elif third_version:
from particle_filtering.pf_uct_3 import PFMCTS3 as PFMCTS
else:
from particle_filtering.pf_uct import PFMCTS
else:
from particle_filtering.pf_mcts_edo import PFMCTS
else:
from pure_mcts.mcts import MCTS
from pure_mcts.mcts_dpw import MCTSStochastic
if parallelize_evaluation:
print("The evaluation will be parallel")
parameter_list = {
"game": game,
"n_ep": n_ep,
"n_mcts": n_mcts,
"max_ep_len": max_ep_len,
"lr": lr,
"c": c,
"gamma": gamma,
"data_size": data_size,
"batch_size": batch_size,
"temp": temp,
"n_hidden_layers": n_hidden_layers,
"n_hidden_units": n_hidden_units,
"stochastic": stochastic,
"eval_freq": eval_freq,
"eval_episodes": eval_episodes,
"alpha": alpha,
"n_epochs": n_epochs,
"out_dir": numpy_dump_dir,
"pre_process": pre_process,
"visualize": visualize,
"game_params": game_params,
"n_workers": n_workers,
"use_sampler": use_sampler,
"variance": variance,
"depth_based_bias": depth_based_bias,
"unbiased": unbiased,
"second_version": second_version,
'third_version': third_version
}
if out_dir is not None:
if not os.path.exists(out_dir):
os.makedirs(out_dir)
with open(os.path.join(out_dir, "parameters.txt"), 'w') as d:
d.write(json.dumps(parameter_list))
#logger = Logger(parameter_list, game, show=show_plots)
if DEBUG_TAXI:
from utils.visualization.taxi import TaxiVisualizer
with open(game_params["grid"]) as f:
m = f.readlines()
matrix = []
for r in m:
row = []
for ch in r.strip('\n'):
row.append(ch)
matrix.append(row)
visualizer = TaxiVisualizer(matrix)
f.close()
exit()
''' Outer training loop '''
if pre_process is not None:
pre_process()
# numpy_dump_dir = logger.numpy_dumps_dir
#
# if not os.path.exists(numpy_dump_dir):
# os.makedirs(numpy_dump_dir)
episode_returns = [] # storage
timepoints = []
# Environments
if game == 'Trading-v0':
game_params['save_dir'] = out_dir #logger.save_dir
Env = make_game(game, game_params)
num_actions = Env.action_space.n
sampler = None
if use_sampler and not (unbiased or biased):
def make_pi(action_space):
def pi(s):
return np.random.randint(low=0, high=action_space.n)
return pi
def make_env():
return make_game(game, game_params)
sampler = ParallelSampler(make_pi=make_pi,
make_env=make_env,
n_particles=particles,
n_workers=n_workers,
seed=10)
is_atari = is_atari_game(Env)
mcts_env = make_game(game, game_params) if is_atari else None
online_scores = []
offline_scores = []
# Setup the parameters for generating the search environments
if game == "RaceStrategy-v1":
mcts_maker, mcts_params, c_dpw = load_race_agents_config(
'envs/configs/race_strategy_full.json', gamma)
else:
mcts_params = dict(gamma=gamma)
if particles:
if not (biased or unbiased):
mcts_params['particles'] = particles
mcts_params['sampler'] = sampler
elif biased:
mcts_params['alpha'] = alpha
mcts_maker = PFMCTS
mcts_params['depth_based_bias'] = depth_based_bias
if unbiased:
mcts_params['variance'] = variance
mcts_maker = OL_MCTS
elif stochastic:
mcts_params['alpha'] = alpha
mcts_params['depth_based_bias'] = depth_based_bias
mcts_maker = MCTSStochastic
else:
mcts_maker = MCTS
# Prepare the database for storing training data to be sampled
db = Database(max_size=data_size, batch_size=batch_size)
# TODO extract dimensions to avoid allocating model
# Setup the model
model_params = {
"Env": Env,
"lr": lr,
"n_hidden_layers": n_hidden_layers,
"n_hidden_units": n_hidden_units,
"joint_networks": True
}
model_wrapper = ModelWrapper(**model_params)
t_total = 0 # total steps
R_best = -np.Inf
a_best = None
seed_best = None
# Variables for storing values to be plotted
avgs = []
stds = []
# Run the episodes
for ep in range(n_ep):
if DEBUG_TAXI:
visualizer.reset()
##### Policy evaluation step #####
if eval_freq > 0 and ep % eval_freq == 0: # and ep > 0
print(
'--------------------------------\nEvaluating policy for {} episodes!'
.format(eval_episodes))
seed = np.random.randint(1e7) # draw some Env seed
Env.seed(seed)
s = Env.reset()
if parallelize_evaluation:
penv = None
pgame = {
"game_maker": make_game,
"game": game,
"game_params": game_params
}
else:
penv = Env
pgame = None
model_file = os.path.join(out_dir, "model.h5")
# model_wrapper.save(model_file)
if game == "RaceStrategy-v1":
env_wrapper = RaceWrapper(s,
mcts_maker,
model_file,
model_params,
mcts_params,
is_atari,
n_mcts,
budget,
mcts_env,
c_dpw,
temp,
env=penv,
game_maker=pgame,
mcts_only=mcts_only,
scheduler_params=scheduler_params)
else:
env_wrapper = Wrapper(s,
mcts_maker,
model_file,
model_params,
mcts_params,
is_atari,
n_mcts,
budget,
mcts_env,
c_dpw,
temp,
env=penv,
game_maker=pgame,
mcts_only=mcts_only,
scheduler_params=scheduler_params)
# Run the evaluation
if parallelize_evaluation:
total_reward, reward_per_timestep, lens, action_counts = \
parallelize_eval_policy(env_wrapper, n_episodes=eval_episodes, verbose=False, max_len=max_ep_len,
max_workers=max_workers, out_dir=out_dir)
else:
total_reward, reward_per_timestep, lens, action_counts = \
eval_policy(env_wrapper, n_episodes=eval_episodes, verbose=False, max_len=max_ep_len,
visualize=visualize, out_dir=out_dir, render=render)
# offline_scores.append([np.min(rews), np.max(rews), np.mean(rews), np.std(rews),
# len(rews), np.mean(lens)])
offline_scores.append(
[total_reward, reward_per_timestep, lens, action_counts])
#np.save(numpy_dump_dir + '/offline_scores.npy', offline_scores)
# Store and plot data
avgs.append(np.mean(total_reward))
stds.append(np.std(total_reward))
#logger.plot_evaluation_mean_and_variance(avgs, stds)
##### Policy improvement step #####
if not mcts_only:
start = time.time()
s = start_s = Env.reset()
R = 0.0 # Total return counter
a_store = []
seed = np.random.randint(1e7) # draw some Env seed
Env.seed(seed)
if is_atari:
mcts_env.reset()
mcts_env.seed(seed)
if eval_freq > 0 and ep % eval_freq == 0:
print("\nCollecting %d episodes" % eval_freq)
mcts = mcts_maker(
root_index=s,
root=None,
model=model_wrapper,
na=model_wrapper.action_dim,
**mcts_params) # the object responsible for MCTS searches
print("\nPerforming MCTS steps\n")
ep_steps = 0
start_targets = []
for st in range(max_ep_len):
print_step = max_ep_len // 10
if st % print_step == 0:
print('Step ' + str(st + 1) + ' of ' + str(max_ep_len))
# MCTS step
if not is_atari:
mcts_env = None
mcts.search(n_mcts=n_mcts, c=c, Env=Env,
mcts_env=mcts_env) # perform a forward search
if visualize:
mcts.visualize()
state, pi, V = mcts.return_results(
temp) # extract the root output
# Save targets for starting state to debug
if np.array_equal(start_s, state):
if DEBUG:
print("Pi target for starting state:", pi)
start_targets.append((V, pi))
db.store((state, V, pi))
# Make the true step
a = np.random.choice(len(pi), p=pi)
a_store.append(a)
s1, r, terminal, _ = Env.step(a)
# Perform command line visualization if necessary
if DEBUG_TAXI:
olds, olda = copy.deepcopy(s1), copy.deepcopy(a)
visualizer.visualize_taxi(olds, olda)
print("Reward:", r)
R += r
t_total += n_mcts # total number of environment steps (counts the mcts steps)
ep_steps = st + 1
if terminal:
break # Stop the episode if we encounter a terminal state
else:
mcts.forward(a, s1, r) # Otherwise proceed
# Finished episode
if DEBUG:
print("Train episode return:", R)
print("Train episode actions:", a_store)
episode_returns.append(R) # store the total episode return
online_scores.append(R)
timepoints.append(
t_total) # store the timestep count of the episode return
#store_safely(numpy_dump_dir, '/result', {'R': episode_returns, 't': timepoints})
#np.save(numpy_dump_dir + '/online_scores.npy', online_scores)
if DEBUG or True:
print(
'Finished episode {} in {} steps, total return: {}, total time: {} sec'
.format(ep, ep_steps, np.round(R, 2),
np.round((time.time() - start), 1)))
# Plot the online return over training episodes
#logger.plot_online_return(online_scores)
if R > R_best:
a_best = a_store
seed_best = seed
R_best = R
print()
# Train only if the model has to be used
if not mcts_only:
# Train
try:
print("\nTraining network")
ep_V_loss = []
ep_pi_loss = []
for _ in range(n_epochs):
# Reshuffle the dataset at each epoch
db.reshuffle()
batch_V_loss = []
batch_pi_loss = []
# Batch training
for sb, Vb, pib in db:
if DEBUG:
print("sb:", sb)
print("Vb:", Vb)
print("pib:", pib)
loss = model_wrapper.train(sb, Vb, pib)
batch_V_loss.append(loss[1])
batch_pi_loss.append(loss[2])
ep_V_loss.append(mean(batch_V_loss))
ep_pi_loss.append(mean(batch_pi_loss))
# Plot the loss over training epochs
#logger.plot_loss(ep, ep_V_loss, ep_pi_loss)
except Exception as e:
print("Something wrong while training:", e)
# model.save(out_dir + 'model')
# Plot the loss over different episodes
#logger.plot_training_loss_over_time()
pi_start = model_wrapper.predict_pi(start_s)
V_start = model_wrapper.predict_V(start_s)
print("\nStart policy: ", pi_start)
print("Start value:", V_start)
#logger.log_start(ep, pi_start, V_start, start_targets)
# Return results
if use_sampler:
sampler.close()
return episode_returns, timepoints, a_best, seed_best, R_best, offline_scores
def make_env():
return make_game(game, game_params)
def __init__(self, hps, cluster_spec=None):
self.hps = hps
if hps.distributed:
ps_servers = [
"/job:ps/task:{}".format(ps_num) for ps_num in range(hps.n_ps)
]
config = tf.ConfigProto(
device_filters=ps_servers +
['/job:ps', '/job:worker/task:{}'.format(hps.job_index)],
#device_count={"CPU": hps.num_agents, "GPU" : 0},
#allow_soft_placement=True,
inter_op_parallelism_threads=2,
intra_op_parallelism_threads=1,
log_device_placement=False)
server = tf.train.Server(cluster_spec,
config=config,
job_name="worker",
task_index=hps.job_index)
else:
server, config = None, None
# Environment
Env = make_game(hps.game)
try:
hps.max_ep_len = Env._max_episode_steps - 1
logger.info('Set max steps per episode to {}'.format(
hps.max_ep_len))
except:
logger.info(
'Environment does not have a time limit wrapper, using {} max steps per episode'
.format(hps.max_ep_len))
hps.action_dim, hps.action_discrete = check_space(Env.action_space)
hps.state_dim, hps.state_discrete = check_space(Env.observation_space)
if not hps.action_discrete:
raise ValueError('Continuous action space not implemented')
# Seed
seed = np.random.randint(1e8) + 7 * hps.job_index
np.random.seed(seed)
random.seed(seed)
# Network
model, target_model, copy_op, global_model, global_target_model, global_copy_op, sync_op = make_network(
hps, cluster_spec)
logger.info('Total number of trainable parameters {} million'.format(
model_description(model.var_list) / (1e6)))
if not hps.distributed:
with tf.Session() as sess:
logger.info('Initializing ..')
sess.run(tf.global_variables_initializer())
run(Env, hps, sess, model, target_model, copy_op, global_model,
global_target_model, global_copy_op, sync_op)
else:
# make init op
global_init_op, local_init_op, global_variables = make_init_ops()
sv = tf.train.Supervisor(
is_chief=(hps.job_index == 0),
init_op=global_init_op,
local_init_op=local_init_op,
ready_op=tf.report_uninitialized_variables(
tf.global_variables()),
#logdir = hps.result_dir,
)
#print('Im worker {} before the supervisor'.format(hps.job_index))
with sv.managed_session(server.target,
config=config) as sess, sess.as_default():
#print('Im worker {} after the supervisor'.format(hps.job_index))
sess.run(sync_op)
run(Env, hps, sess, model, target_model, copy_op, global_model,
global_target_model, global_copy_op, sync_op, sv)
sv.stop()
def run(Env,
hps,
sess,
model,
target_model=None,
copy_op=None,
global_model=None,
global_target_model=None,
global_copy_op=None,
sync_op=None,
sv=None):
begin = overall_begin = time.time()
print('Im worker {} and starting'.format(hps.job_index))
# Database
D = Database(data_size=hps.n_ep_collect * hps.max_ep_len *
hps.n_rep_target * 2,
batch_size=hps.batch_size,
entry_type='sequential')
if hps.replay_size > 0:
D_sars = Replay(max_size=hps.replay_size,
prioritized_frac=hps.prioritized_frac)
#D_sars = Database(data_size=hps.replay_size, batch_size=hps.batch_size,entry_type='sequential')
#saver = tf.train.Saver(max_to_keep=10)
# Counters
Copy_count = Interval_checker(hps.max_ep, hps.t_max, hps.store_copy_freq)
Eval_count = Interval_checker(hps.max_ep, hps.t_max, hps.evaluate_freq)
if hps.game == 'Toy' and hps.visualize:
from rl.envs.toy import ToyPlotter, ToyDomainPlotter
toy_plotter = ToyPlotter()
toy_domain_plotter = ToyDomainPlotter(hps)
elif 'Chain' in hps.game and hps.visualize:
from rl.envs.chain import ChainPlotter, ChainDomainPlotter
chain_plotter = ChainPlotter(Env.correct, n_plot=Env.n)
chain_domain_plotter = ChainDomainPlotter(Env)
# Discretizer = TransformDiscrete(hps.disc_n,hps.disc_min,hps.disc_max)
# ema = EMA()
if 'Chain' in hps.game:
# necessary for plotting visitation counts correctly
test_Env = make_game(hps.game)
test_Env.correct = Env.correct
else:
test_Env = Env
t = 0
ep = 0
log_count = 0
time_result = TimedResults(
n=6) # Average Reward, Empirical Loss, Qsa_norm, grad_norm, loss
#epsilon = AnnealLinear(hps.e_init,hps.e_final,int(hps.anneal_frac*hps.t_max))
time_check = 't < hps.t_max' if not hps.distributed else '(t < hps.t_max) and not sv.should_stop()'
running_mean = 0.0
frac = 0.97
while eval(time_check): # train loop
now = time.time()
if hps.distributed:
sess.run(sync_op)
e = sess.run(model.epsilon)
# Collect data
t_new, t_, av_R, ep_R, data = collect_data(hps, model, sess, Env, e)
# Process new (on-policy) data
D.clear() # clear training database
D, D_sars, Qsa_norm, Qsa_sds = calculate_targets(
data,
D,
D_sars,
model,
sess,
hps,
hps.lambda_,
off_policy=hps.off_policy,
target_model=target_model)
# Fill up database from replay
if D_sars.size > hps.min_replay_size:
extra_needed = hps.replay_frac * D.size
more = hps.batch_size - (D.size + extra_needed) % hps.batch_size
if more > 0:
extra_needed += more # to make full minibatches from rollout
if (extra_needed > 0) and (extra_needed <= D_sars.size):
# draw the samples
sb, ab, rb, s1b, tb = D_sars.sample_random_batch(
extra_needed, True) # sample from replay
D, D_sars, Qsa_norm2 = calculate_targets_off_policy(
sb, ab, rb, s1b, tb, D, D_sars, model, sess, hps,
target_model) # Process the extra data (off-policy)
# Train
gradient_norm, clipped_norm, loss = train(hps, model, sess, D)
# # Put new data in replay database
# if hps.replay_size > 0:
# for rollout in data:
# si,ai,ri,st = rollout.extract()
# tt = np.zeros(si.shape[0])
# tt[-1] = rollout.terminal
# si1 = np.concatenate([si[1:,],st[None,:]],axis=0)
# D_sars.store_from_array(si,ai,ri,si1,tt)
# # Send data to replay database
# if hps.replay_frac > 0:
# if hps.replay_size > 0:
# for j,rollout_data in enumerate(data):
# rollout_data.seed = None # seed becomes irrelevant
# D_replay.put(rollout_data,priority=prios)#-1.0*prios[j])
#
# if hps.replay_size > 0 and len(D_replay.q)>(hps.replay_frac*len(data)*2):
# replay_data = D_replay.get(hps.replay_frac*len(data))
# # replay always off-policy (lambda_=0.0) for sample-based loss
# D,_ = calculate_targets(replay_data,D,model,sess,hps,lambda_=0.0,off_policy=hps.off_policy,target_model=target_model)
# new_prios = 0.0
# #D,_,new_prios = process(replay_data,D,model,sess,hps,target_model=target_model,ema=ema)
# train(hps,model,sess,D)
# D.clear()
# if hps.prioritized_frac>0.0:
# for i in range(len(replay_data)):
# D_replay.put(replay_data[i],priority=-1.0*new_prios[i])
# Counters
_, _, t, ep = sess.run(
[model.inc_t, model.inc_ep, model.global_t, model.global_ep],
feed_dict={
model.t: t_new,
model.ep: len(ep_R)
})
if hps.level == 'debug':
memory_use = resource.getrusage(
resource.RUSAGE_SELF).ru_maxrss / 1024
print(
'Process {} = worker {} loops in {} seconds, memory use {} Mb for replay size {}'
.format(hps.agent_index, hps.job_index,
time.time() - now, memory_use, D_sars.size))
#else:
# print('Memory use {} Mb'.format(memory_use))
if (not hps.distributed) or (hps.distributed and hps.job_index == 0):
# Store
episode_reward, average_reward = np.mean(ep_R), np.mean(av_R)
time_result.add([ep - (len(ep_R) / 2)], [average_reward],
[episode_reward], [np.mean(Qsa_norm)],
[gradient_norm], [loss], [int(t - t_new / 2)])
#logger.info('Evaluated: Ep {:5d}, t {:5d}: Ep return {:4.2f}, Running mean {:4.2f}, Qsa_norm {:4.2f}, grad_norm {:4.2f}, clipped_norm {:4.2f}, loss {:4.2f}, episode_length {:3.1f}'.format(ep,t,episode_reward,running_mean,np.mean(Qsa_norm),gradient_norm,clipped_norm,loss,np.mean(t_)))
ep_curve, av_R_curve, ep_R_curve, Qsa_norm_curve, grad_norm_curve, loss_curve, t_curve = time_result.extract(
)
downsample_store(hps,
ep_curve,
ep_R_curve,
av_R_curve,
Qsa_norm_curve,
grad_norm_curve,
loss_curve,
t_curve,
out_len=1000)
# Logging
if (t // hps.log_interval > log_count) and (not hps.slurm):
running_mean = frac * running_mean + (1 - frac) * np.mean(ep_R)
logger.info(
'Ep {:4d}, t {:5d}: Ep Return {:4.2f}, Run_mean {:4.2f}, Qsa_mean {:4.2f}, Qsa_sd {:4.2f}, grad_norm {:4.2f}, clip_norm {:4.2f}, loss {:4.2f}, ep_len {:3.1f}'
.format(ep, t, np.mean(ep_R), running_mean,
np.mean(Qsa_norm), Qsa_sds, gradient_norm,
clipped_norm, loss, np.mean(t_)))
log_count += 1
# Copy target network
if Copy_count.should_update(t, ep):
# Target net
if hps.target_net:
if hps.distributed:
sess.run(global_copy_op)
else:
sess.run(copy_op)
# for uncer == 'log_bay' --> set new uncertainty estimates
# sample larger X and Y batch:
if hps.uncer == 'log_bay':
D.clear()
log_bay_sample = np.min([D_sars.size, 3000])
sb, ab, rb, s1b, tb = D_sars.sample_random_batch(
log_bay_sample, True) # sample from replay
D, D_sars, Qsa_norm2 = calculate_targets_off_policy(
sb, ab, rb, s1b, tb, D, D_sars, model, sess, hps,
target_model) # Process the extra data (off-policy)
s_batch, a_batch, y_batch = D.sample_random_batch(
log_bay_sample)
seed = [np.random.randint(1e15),
np.random.randint(1e15)
] # just need to feed something new for each batch
feed_dict = {
model.x: s_batch,
model.a: a_batch,
model.y: y_batch,
model.seed: seed
}
sess.run(model.lin_bay_update, feed_dict=feed_dict)
# Visualize for Toy/Chain
if hps.game == 'Toy' and hps.visualize:
if Eval_count.should_update(t, ep):
toy_plotter.update(sess, model, hps, ep)
toy_domain_plotter.update(Env.counts)
elif 'Chain' in hps.game and hps.visualize:
if Eval_count.should_update(t, ep):
chain_plotter.update(sess, model, hps, ep)
chain_domain_plotter.update(Env.counts)
elif hps.visualize:
if Eval_count.should_update(t, ep):
episode_reward, average_reward = evaluate(
test_Env, hps, model, sess)
# Store model
#if rep == 0:
# saver.save(sess,hps.result_dir+make_name('',hps.item1,item1,hps.item2,item2,hps.item3,item3,hps.item4,item4)+'model.ckpt')
# if Eval_count.should_update(t,ep):
# if hps.distributed:
# sess.run(sync_op)
# episode_reward,average_reward = evaluate(test_Env,hps,model,sess)
# time_result.add([ep - (len(ep_R)/2)],[average_reward],[episode_reward],[np.mean(ep_R)],[np.mean(Qsa_norm)],[gradient_norm],[loss])
# logger.info('Evaluated: Ep {:5d}, t {:5d}: Ep return {:4.2f}, Running mean {:4.2f}, Qsa_norm {:4.2f}, grad_norm {:4.2f}, clipped_norm {:4.2f}, loss {:4.2f}, episode_length {:3.1f}'.format(ep,t,episode_reward,running_mean,np.mean(Qsa_norm),gradient_norm,clipped_norm,loss,np.mean(t_)))
# ep_curve,av_R_curve,ep_R_curve,Qsa_norm_curve,grad_norm_curve,loss_curve = time_result.extract()
# downsample_store(hps,ep_curve,ep_R_curve,av_R_curve,Qsa_norm_curve,grad_norm_curve,loss_curve,out_len=1000)
if (ep > hps.max_ep) or (t > hps.t_max):
if hps.distributed:
sv.request_stop()
break # max number of episodes overrules max number of steps
elapsed = (time.time() - overall_begin) / 60
logger.info(
'Reached {} episodes in {} timesteps, took {} hours = {} minutes'.
format(ep, t, elapsed / 60, elapsed))
if (not hps.distributed) or (hps.disributed and hps.job_index == 0):
ep_curve, av_R_curve, ep_R_curve, Qsa_norm_curve, grad_norm_curve, loss_curve, t_curve = time_result.extract(
)
downsample_store(hps,
ep_curve,
ep_R_curve,
av_R_curve,
Qsa_norm_curve,
grad_norm_curve,
loss_curve,
t_curve,
out_len=1000)
# Compute best result (last 10%)
fraction = 0.1
save_from_index = int(np.ceil(len(ep_curve) * fraction))
ep_R_best = np.mean(ep_R_curve[save_from_index:])
av_R_best = np.mean(av_R_curve[save_from_index:])
np.savetxt(hps.result_dir + 'best_results.txt',
np.append(ep_R_best, av_R_best),
fmt='%.3g')
def agent(game, n_ep, n_mcts, max_ep_len, lr, c, gamma, data_size, batch_size,
temp, n_hidden_layers, n_hidden_units):
""" Outer training loop """
seed_best = None
a_best = None
episode_returns = [] # storage
timepoints = []
# environments
env = make_game(game)
is_atari = is_atari_game(env)
mcts_env = make_game(game) if is_atari else None
database = Database(max_size=data_size, batch_size=batch_size)
model = Model(env=env,
lr=lr,
n_hidden_layers=n_hidden_layers,
n_hidden_units=n_hidden_units)
t_total = 0 # total steps
r_best = -np.Inf
for ep in range(n_ep):
start = time.time()
s = env.reset()
r2 = 0.0 # Total return counter
a_store = []
seed = np.random.randint(1e7) # draw some env seed
env.seed(seed)
if is_atari:
mcts_env.reset()
mcts_env.seed(seed)
mcts = MCTS(root_index=s,
model=model,
na=model.action_dim,
gamma=gamma) # the object responsible for MCTS searches
for t in range(max_ep_len):
# MCTS step
mcts.search(n_mcts=n_mcts, c=c, env=env,
mcts_env=mcts_env) # perform a forward search
state, pi, v = mcts.return_results(temp) # extract the root output
database.store((state, v, pi))
# Make the true step
a = np.random.choice(len(pi), p=pi)
a_store.append(a)
s1, r, terminal, _ = env.step(a)
r2 += r
# total number of environment steps (counts the mcts steps)
t_total += n_mcts
if terminal:
break
else:
mcts.forward(a, s1)
# Finished episode
episode_returns.append(r2) # store the total episode return
timepoints.append(
t_total) # store the timestep count of the episode return
store_safely(os.getcwd(), 'result', {
'r': episode_returns,
't': timepoints
})
if r2 > r_best:
a_best = a_store
seed_best = seed
r_best = r2
print(
'Finished episode {}, total return: {}, total time: {} sec'.format(
ep, np.round(r2, 2), np.round((time.time() - start), 1)))
# Train
database.reshuffle()
for epoch in range(1):
for sb, v_batch, pi_batch in database:
model.train(sb, v_batch, pi_batch)
# return results
return episode_returns, timepoints, a_best, seed_best, r_best
def train_trpo(game, num_timesteps, eval_episodes, seed, horizon, out_dir='.', load_path=None, checkpoint_path_in=None,
gamma=0.99, timesteps_per_batch=500, num_layers=0, num_hidden=32, checkpoint_freq=20, max_kl=0.01):
start_time = time.time()
clip = None
dir = 'game'
game_params = {}
# Accept custom grid if the environment requires it
if game == 'Taxi' or game == 'TaxiEasy':
game_params['grid'] = args.grid
game_params['box'] = True
if game in ['RaceStrategy-v0', 'Cliff-v0']:
game_params['horizon'] = horizon
# env = Race(gamma=gamma, horizon=horizon, )
# env_eval = Race(gamma=gamma, horizon=horizon)
env = make_game(args.game, game_params)
env_eval = make_game(args.game, game_params)
directory_output = (dir + '/trpo_' + str(num_layers) + '_'+ str(num_hidden) + '_'+ str(max_kl) + '/')
def eval_policy_closure(**args):
return eval_policy(env=env_eval, gamma=gamma, **args)
tf.set_random_seed(seed)
sess = U.single_threaded_session()
sess.__enter__()
rank = MPI.COMM_WORLD.Get_rank()
time_str = str(start_time)
if rank == 0:
logger.configure(dir=out_dir + '/' + directory_output + '/logs',
format_strs=['stdout', 'csv'], suffix=time_str)
else:
logger.configure(format_strs=[])
logger.set_level(logger.DISABLED)
network = mlp(num_hidden=num_hidden, num_layers=num_layers)
optimized_policy = trpo_mpi.learn(network=network, env=env, eval_policy=eval_policy_closure, timesteps_per_batch=timesteps_per_batch,
max_kl=max_kl, cg_iters=10, cg_damping=1e-3,
total_timesteps=num_timesteps, gamma=gamma, lam=1.0, vf_iters=3, vf_stepsize=1e-4,
checkpoint_freq=checkpoint_freq,
checkpoint_dir_out=out_dir + '/' + directory_output + '/models/' + time_str + '/',
load_path=load_path, checkpoint_path_in=checkpoint_path_in,
eval_episodes=eval_episodes,
init_std=1,
trainable_variance=True,
trainable_bias=True,
clip=clip)
s = env.reset()
done = False
states = []
actions = []
s = 0
delta_state = 0.2
while s < env.dim[0]:
a, _, _, _ = optimized_policy.step([s])
states.append(s)
actions.append(a[0])
s += delta_state
s = env.reset()
plt.plot(states, actions)
plt.show()
print('TOTAL TIME:', time.time() - start_time)
print("Time taken: %f seg" % ((time.time() - start_time)))
print("Time taken: %f hours" % ((time.time() - start_time) / 3600))
env.close()