def main(args):
import logging
logging.basicConfig(level=logging.INFO, filename='log')
if (type(args) is list):
args = make_args(args)
if not os.path.exists(args.outdir):
os.makedirs(args.outdir)
# Set a random seed used in ChainerRL.
# If you use more than one processes, the results will be no longer
# deterministic even with the same random seed.
misc.set_random_seed(args.seed)
# Set different random seeds for different subprocesses.
# If seed=0 and processes=4, subprocess seeds are [0, 1, 2, 3].
# If seed=1 and processes=4, subprocess seeds are [4, 5, 6, 7].
process_seeds = np.arange(args.processes) + args.seed * args.processes
assert process_seeds.max() < 2**31
n_actions = gym.make(args.env).action_space.n
if args.use_lstm:
model = acer.ACERSharedModel(
shared=links.Sequence(links.NIPSDQNHead(), L.LSTM(256, 256)),
pi=links.Sequence(L.Linear(256, n_actions), SoftmaxDistribution),
q=links.Sequence(L.Linear(256, n_actions), DiscreteActionValue),
)
else:
model = acer.ACERSharedModel(
shared=links.NIPSDQNHead(),
pi=links.Sequence(L.Linear(256, n_actions), SoftmaxDistribution),
q=links.Sequence(L.Linear(256, n_actions), DiscreteActionValue),
)
opt = rmsprop_async.RMSpropAsync(lr=7e-4, eps=4e-3, alpha=0.99)
opt.setup(model)
opt.add_hook(chainer.optimizer.GradientClipping(40))
if args.weight_decay > 0:
opt.add_hook(NonbiasWeightDecay(args.weight_decay))
replay_buffer = EpisodicReplayBuffer(10**6 // args.processes)
def phi(x):
# Feature extractor
return np.asarray(x, dtype=np.float32) / 255
agent = acer.ACER(model,
opt,
t_max=args.t_max,
gamma=0.99,
replay_buffer=replay_buffer,
n_times_replay=args.n_times_replay,
replay_start_size=args.replay_start_size,
beta=args.beta,
phi=phi)
def make_env(process_idx, test):
# Use different random seeds for train and test envs
process_seed = process_seeds[process_idx]
env_seed = 2**31 - 1 - process_seed if test else process_seed
env = atari_wrappers.wrap_deepmind(atari_wrappers.make_atari(
args.env, max_frames=args.max_frames),
episode_life=not test,
clip_rewards=not test)
env.seed(int(env_seed))
if args.monitor:
env = chainerrl.wrappers.Monitor(
env, args.outdir, mode='evaluation' if test else 'training')
if args.render:
env = chainerrl.wrappers.Render(env)
return env
def make_env_check():
# Use different random seeds for train and test envs
env_seed = args.seed
env = atari_wrappers.wrap_deepmind(atari_wrappers.make_atari(
args.env, max_frames=args.max_frames),
episode_life=True,
clip_rewards=True)
env.seed(int(env_seed))
return env
if args.load_agent:
agent.load(args.load_agent)
if (args.mode == 'train'):
# Linearly decay the learning rate to zero
def lr_setter(env, agent, value):
agent.optimizer.lr = value
lr_decay_hook = experiments.LinearInterpolationHook(
args.steps, args.lr, 0, lr_setter)
experiments.train_agent_async(
agent=agent,
outdir=args.outdir,
processes=args.processes,
make_env=make_env,
profile=args.profile,
steps=args.steps,
eval_n_steps=None,
eval_n_episodes=args.eval_n_runs,
eval_interval=args.eval_interval,
global_step_hooks=[lr_decay_hook],
save_best_so_far_agent=False,
)
elif (args.mode == 'check'):
return tools.make_video.check(env=make_env_check(),
agent=agent,
save_mp4=args.save_mp4)
elif (args.mode == 'growth'):
return tools.make_video.growth(env=make_env_check(),
agent=agent,
outdir=args.outdir,
max_num=args.max_frames,
save_mp4=args.save_mp4)
def main(args):
import logging
logging.basicConfig(level=logging.INFO, filename='log')
if (type(args) is list):
args = make_args(args)
if not os.path.exists(args.outdir):
os.makedirs(args.outdir)
# Set a random seed used in ChainerRL.
# If you use more than one process (i.e. processes > 1),
# the results will be no longer be deterministic
# even with the same random seed.
misc.set_random_seed(args.seed)
# Set different random seeds for different subprocesses.
# If seed=0 and processes=4, subprocess seeds are [0, 1, 2, 3].
# If seed=1 and processes=4, subprocess seeds are [4, 5, 6, 7].
process_seeds = np.arange(args.processes) + args.seed * args.processes
assert process_seeds.max() < 2**31
n_actions = gym.make(args.env).action_space.n
if args.use_lstm:
model = A3CLSTM(n_actions)
else:
model = A3CFF(n_actions)
# Draw the computational graph and save it in the output directory.
fake_obs = chainer.Variable(np.zeros((4, 84, 84), dtype=np.float32)[None],
name='observation')
with chainerrl.recurrent.state_reset(model):
# The state of the model is reset again after drawing the graph
chainerrl.misc.draw_computational_graph([model(fake_obs)],
os.path.join(
args.outdir, 'model'))
opt = rmsprop_async.RMSpropAsync(lr=7e-4, eps=1e-1, alpha=0.99)
opt.setup(model)
opt.add_hook(chainer.optimizer.GradientClipping(40))
if args.weight_decay > 0:
opt.add_hook(NonbiasWeightDecay(args.weight_decay))
def phi(x):
# Feature extractor
return np.asarray(x, dtype=np.float32) / 255
agent = a3c.A3C(model,
opt,
t_max=args.t_max,
gamma=0.99,
beta=args.beta,
phi=phi)
def make_env(process_idx, test):
# Use different random seeds for train and test envs
process_seed = process_seeds[process_idx]
env_seed = 2**31 - 1 - process_seed if test else process_seed
env = atari_wrappers.wrap_deepmind(atari_wrappers.make_atari(
args.env, max_frames=args.max_frames),
episode_life=not test,
clip_rewards=not test)
env.seed(int(env_seed))
if args.monitor:
env = chainerrl.wrappers.Monitor(
env, args.outdir, mode='evaluation' if test else 'training')
if args.render:
env = chainerrl.wrappers.Render(env)
return env
def make_env_check():
# Use different random seeds for train and test envs
env_seed = args.seed
env = atari_wrappers.wrap_deepmind(atari_wrappers.make_atari(
args.env, max_frames=args.max_frames),
episode_life=True,
clip_rewards=True)
env.seed(int(env_seed))
return env
if args.load_agent:
agent.load(args.load_agent)
if (args.mode == 'train'):
# Linearly decay the learning rate to zero
def lr_setter(env, agent, value):
agent.optimizer.lr = value
lr_decay_hook = experiments.LinearInterpolationHook(
args.steps, args.lr, 0, lr_setter)
experiments.train_agent_async(
agent=agent,
outdir=args.outdir,
processes=args.processes,
make_env=make_env,
profile=args.profile,
steps=args.steps,
step_offset=args.step_offset,
checkpoint_freq=args.checkpoint_frequency,
eval_n_steps=None,
eval_n_episodes=args.eval_n_runs,
eval_interval=args.eval_interval,
global_step_hooks=[lr_decay_hook],
save_best_so_far_agent=False,
log_type=args.log_type)
elif (args.mode == 'check'):
return tools.make_video.check(env=make_env_check(),
agent=agent,
save_mp4=args.save_mp4)
elif (args.mode == 'growth'):
return tools.make_video.growth(env=make_env_check(),
agent=agent,
outdir=args.outdir,
max_num=args.max_frames,
save_mp4=args.save_mp4)
def main(args):
import logging
logging.basicConfig(level=logging.INFO, filename='log')
if (type(args) is list):
args = make_args(args)
if not os.path.exists(args.outdir):
os.makedirs(args.outdir)
# Set a random seed used in ChainerRL.
# If you use more than one processes, the results will be no longer
# deterministic even with the same random seed.
misc.set_random_seed(args.seed)
# Set different random seeds for different subprocesses.
# If seed=0 and processes=4, subprocess seeds are [0, 1, 2, 3].
# If seed=1 and processes=4, subprocess seeds are [4, 5, 6, 7].
process_seeds = np.arange(args.processes) + args.seed * args.processes
assert process_seeds.max() < 2**32
def make_env(process_idx, test):
env = gym.make(args.env)
# Use different random seeds for train and test envs
process_seed = int(process_seeds[process_idx])
env_seed = 2**32 - 1 - process_seed if test else process_seed
env.seed(env_seed)
# Cast observations to float32 because our model uses float32
env = chainerrl.wrappers.CastObservationToFloat32(env)
if args.monitor and process_idx == 0:
env = chainerrl.wrappers.Monitor(env, args.outdir)
if not test:
# Scale rewards (and thus returns) to a reasonable range so that
# training is easier
env = chainerrl.wrappers.ScaleReward(env, args.reward_scale_factor)
if args.render and process_idx == 0 and not test:
env = chainerrl.wrappers.Render(env)
return env
sample_env = gym.make(args.env)
timestep_limit = sample_env.spec.tags.get(
'wrapper_config.TimeLimit.max_episode_steps')
obs_space = sample_env.observation_space
action_space = sample_env.action_space
if isinstance(action_space, spaces.Box):
model = acer.ACERSDNSeparateModel(
pi=policies.FCGaussianPolicy(
obs_space.low.size,
action_space.low.size,
n_hidden_channels=args.n_hidden_channels,
n_hidden_layers=args.n_hidden_layers,
bound_mean=True,
min_action=action_space.low,
max_action=action_space.high),
v=v_functions.FCVFunction(obs_space.low.size,
n_hidden_channels=args.n_hidden_channels,
n_hidden_layers=args.n_hidden_layers),
adv=q_functions.FCSAQFunction(
obs_space.low.size,
action_space.low.size,
n_hidden_channels=args.n_hidden_channels // 4,
n_hidden_layers=args.n_hidden_layers),
)
else:
model = acer.ACERSeparateModel(
pi=links.Sequence(
L.Linear(obs_space.low.size, args.n_hidden_channels), F.relu,
L.Linear(args.n_hidden_channels,
action_space.n,
initialW=LeCunNormal(1e-3)), SoftmaxDistribution),
q=links.Sequence(
L.Linear(obs_space.low.size, args.n_hidden_channels), F.relu,
L.Linear(args.n_hidden_channels,
action_space.n,
initialW=LeCunNormal(1e-3)), DiscreteActionValue),
)
opt = rmsprop_async.RMSpropAsync(lr=args.lr,
eps=args.rmsprop_epsilon,
alpha=0.99)
opt.setup(model)
opt.add_hook(chainer.optimizer.GradientClipping(40))
replay_buffer = EpisodicReplayBuffer(args.replay_capacity)
agent = acer.ACER(model,
opt,
t_max=args.t_max,
gamma=0.99,
replay_buffer=replay_buffer,
n_times_replay=args.n_times_replay,
replay_start_size=args.replay_start_size,
disable_online_update=args.disable_online_update,
use_trust_region=True,
trust_region_delta=args.trust_region_delta,
truncation_threshold=args.truncation_threshold,
beta=args.beta)
if args.load_agent:
agent.load(args.load_agent)
if (args.mode == 'train'):
experiments.train_agent_async(agent=agent,
outdir=args.outdir,
processes=args.processes,
make_env=make_env,
profile=args.profile,
steps=args.steps,
step_offset=args.step_offset,
checkpoint_freq=args.checkpoint_freq,
log_type=args.log_type,
eval_n_steps=None,
eval_n_episodes=args.eval_n_runs,
eval_interval=args.eval_interval,
max_episode_len=timestep_limit)
elif (args.mode == 'check'):
from matplotlib import animation
import matplotlib.pyplot as plt
env = make_env(0, True)
frames = []
for i in range(3):
obs = env.reset()
done = False
R = 0
t = 0
while not done and t < 200:
frames.append(env.render(mode='rgb_array'))
action = agent.act(obs)
obs, r, done, _ = env.step(action)
R += r
t += 1
print('test episode:', i, 'R:', R)
agent.stop_episode()
env.close()
from IPython.display import HTML
plt.figure(figsize=(frames[0].shape[1] / 72.0,
frames[0].shape[0] / 72.0),
dpi=72)
patch = plt.imshow(frames[0])
plt.axis('off')
def animate(i):
patch.set_data(frames[i])
anim = animation.FuncAnimation(plt.gcf(),
animate,
frames=len(frames),
interval=50)
anim.save(args.save_mp4)
return anim
def main(args):
import logging
logging.basicConfig(level=logging.INFO, filename='log')
if (type(args) is list):
args = make_args(args)
if not os.path.exists(args.outdir):
os.makedirs(args.outdir)
# Set a random seed used in ChainerRL.
# If you use more than one processes, the results will be no longer
# deterministic even with the same random seed.
misc.set_random_seed(args.seed)
# Set different random seeds for different subprocesses.
# If seed=0 and processes=4, subprocess seeds are [0, 1, 2, 3].
# If seed=1 and processes=4, subprocess seeds are [4, 5, 6, 7].
process_seeds = np.arange(args.processes) + args.seed * args.processes
assert process_seeds.max() < 2**31
def make_env(process_idx, test):
# Use different random seeds for train and test envs
process_seed = process_seeds[process_idx]
env_seed = 2**31 - 1 - process_seed if test else process_seed
env = atari_wrappers.wrap_deepmind(atari_wrappers.make_atari(
args.env, max_frames=args.max_frames),
episode_life=not test,
clip_rewards=not test)
env.seed(int(env_seed))
if test:
# Randomize actions like epsilon-greedy in evaluation as well
env = chainerrl.wrappers.RandomizeAction(env, 0.05)
if args.monitor:
env = chainerrl.wrappers.Monitor(
env, args.outdir, mode='evaluation' if test else 'training')
if args.render:
env = chainerrl.wrappers.Render(env)
return env
sample_env = make_env(0, test=False)
action_space = sample_env.action_space
assert isinstance(action_space, spaces.Discrete)
# Define a model and its optimizer
q_func = links.Sequence(links.NIPSDQNHead(), L.Linear(256, action_space.n),
DiscreteActionValue)
opt = rmsprop_async.RMSpropAsync(lr=args.lr, eps=1e-1, alpha=0.99)
opt.setup(q_func)
def phi(x):
# Feature extractor
return np.asarray(x, dtype=np.float32) / 255
# Make process-specific agents to diversify exploration
def make_agent(process_idx):
# Random epsilon assignment described in the original paper
rand = random.random()
if rand < 0.4:
epsilon_target = 0.1
elif rand < 0.7:
epsilon_target = 0.01
else:
epsilon_target = 0.5
explorer = explorers.LinearDecayEpsilonGreedy(
1, epsilon_target, args.final_exploration_frames,
action_space.sample)
# Suppress the explorer logger
explorer.logger.setLevel(logging.INFO)
return nsq.NSQ(q_func,
opt,
t_max=5,
gamma=0.99,
i_target=40000,
explorer=explorer,
phi=phi)
if args.demo:
env = make_env(0, True)
agent = make_agent(0)
eval_stats = experiments.eval_performance(env=env,
agent=agent,
n_steps=None,
n_episodes=args.eval_n_runs)
print('n_runs: {} mean: {} median: {} stdev {}'.format(
args.eval_n_runs, eval_stats['mean'], eval_stats['median'],
eval_stats['stdev']))
else:
# Linearly decay the learning rate to zero
def lr_setter(env, agent, value):
agent.optimizer.lr = value
lr_decay_hook = experiments.LinearInterpolationHook(
args.steps, args.lr, 0, lr_setter)
experiments.train_agent_async(
outdir=args.outdir,
processes=args.processes,
make_env=make_env,
make_agent=make_agent,
profile=args.profile,
steps=args.steps,
eval_n_steps=None,
eval_n_episodes=args.eval_n_runs,
eval_interval=args.eval_interval,
global_step_hooks=[lr_decay_hook],
save_best_so_far_agent=False,
)
def main(args):
import logging
logging.basicConfig(level=logging.INFO, stream=sys.stdout, format='')
if (type(args) is list):
args = make_args(args)
if not os.path.exists(args.outdir):
os.makedirs(args.outdir)
# Set a random seed used in ChainerRL.
# If you use async training (--train-async), the results will be no longer
# deterministic even with the same random seed.
misc.set_random_seed(args.seed, gpus=(args.gpu, ))
if args.train_async:
# Set different random seeds for different subprocesses.
# If seed=0 and processes=4, subprocess seeds are [0, 1, 2, 3].
# If seed=1 and processes=4, subprocess seeds are [4, 5, 6, 7].
process_seeds = np.arange(args.processes) + args.seed * args.processes
assert process_seeds.max() < 2**32
def make_env(process_idx, test):
env = gym.make(args.env)
# Use different random seeds for train and test envs
if args.train_async:
process_seed = int(process_seeds[process_idx])
env_seed = 2**32 - 1 - process_seed if test else process_seed
else:
env_seed = 2**32 - 1 - args.seed if test else args.seed
env.seed(env_seed)
# Cast observations to float32 because our model uses float32
env = chainerrl.wrappers.CastObservationToFloat32(env)
if args.monitor and process_idx == 0:
env = chainerrl.wrappers.Monitor(env, args.outdir)
if not test:
# Scale rewards (and thus returns) to a reasonable range so that
# training is easier
env = chainerrl.wrappers.ScaleReward(env, args.reward_scale_factor)
if args.render and process_idx == 0 and not test:
env = chainerrl.wrappers.Render(env)
return env
sample_env = gym.make(args.env)
timestep_limit = sample_env.spec.tags.get(
'wrapper_config.TimeLimit.max_episode_steps')
obs_space = sample_env.observation_space
action_space = sample_env.action_space
# Switch policy types accordingly to action space types
if isinstance(action_space, gym.spaces.Box):
model = chainerrl.agents.pcl.PCLSeparateModel(
pi=chainerrl.policies.FCGaussianPolicy(
obs_space.low.size,
action_space.low.size,
n_hidden_channels=args.n_hidden_channels,
n_hidden_layers=args.n_hidden_layers,
bound_mean=True,
min_action=action_space.low,
max_action=action_space.high,
var_wscale=1e-3,
var_bias=1,
var_type='diagonal',
),
v=chainerrl.v_functions.FCVFunction(
obs_space.low.size,
n_hidden_channels=args.n_hidden_channels,
n_hidden_layers=args.n_hidden_layers,
))
else:
model = chainerrl.agents.pcl.PCLSeparateModel(
pi=chainerrl.policies.FCSoftmaxPolicy(
obs_space.low.size,
action_space.n,
n_hidden_channels=args.n_hidden_channels,
n_hidden_layers=args.n_hidden_layers),
v=chainerrl.v_functions.FCVFunction(
obs_space.low.size,
n_hidden_channels=args.n_hidden_channels,
n_hidden_layers=args.n_hidden_layers,
),
)
if not args.train_async and args.gpu >= 0:
chainer.cuda.get_device_from_id(args.gpu).use()
model.to_gpu(args.gpu)
if args.train_async:
opt = rmsprop_async.RMSpropAsync(lr=args.lr, alpha=0.99)
else:
opt = chainer.optimizers.Adam(alpha=args.lr)
opt.setup(model)
if args.prioritized_replay:
replay_buffer = \
chainerrl.replay_buffer.PrioritizedEpisodicReplayBuffer(
capacity=5 * 10 ** 3,
uniform_ratio=0.1,
default_priority_func=exp_return_of_episode,
wait_priority_after_sampling=False,
return_sample_weights=False)
else:
replay_buffer = chainerrl.replay_buffer.EpisodicReplayBuffer(
capacity=5 * 10**3)
agent = chainerrl.agents.PCL(
model,
opt,
replay_buffer=replay_buffer,
t_max=args.t_max,
gamma=0.99,
tau=args.tau,
rollout_len=args.rollout_len,
n_times_replay=args.n_times_replay,
replay_start_size=args.replay_start_size,
batchsize=args.batchsize,
train_async=args.train_async,
disable_online_update=args.disable_online_update,
backprop_future_values=args.backprop_future_values,
)
if args.load_agent:
agent.load(args.load_agent)
if (args.mode == 'train'):
if args.train_async:
experiments.train_agent_async(agent=agent,
outdir=args.outdir,
processes=args.processes,
make_env=make_env,
profile=args.profile,
steps=args.steps,
step_offset=args.step_offset,
eval_n_steps=None,
eval_n_episodes=args.eval_n_runs,
eval_interval=args.eval_interval,
max_episode_len=timestep_limit)
if args.save_agent:
agent.save(args.save_agent)
else:
experiments.train_agent_with_evaluation(
agent=agent,
env=make_env(0, test=False),
eval_env=make_env(0, test=True),
outdir=args.outdir,
steps=args.steps,
step_offset=args.step_offset,
checkpoint_freq=args.checkpoint_freq,
eval_n_steps=None,
eval_n_episodes=args.eval_n_runs,
eval_interval=args.eval_interval,
train_max_episode_len=timestep_limit)
elif (args.mode == 'check'):
from matplotlib import animation
import matplotlib.pyplot as plt
frames = []
for i in range(3):
obs = env.reset()
done = False
R = 0
t = 0
while not done and t < 200:
frames.append(env.render(mode='rgb_array'))
action = agent.act(obs)
obs, r, done, _ = env.step(action)
R += r
t += 1
print('test episode:', i, 'R:', R)
agent.stop_episode()
env.close()
from IPython.display import HTML
plt.figure(figsize=(frames[0].shape[1] / 72.0,
frames[0].shape[0] / 72.0),
dpi=72)
patch = plt.imshow(frames[0])
plt.axis('off')
def animate(i):
patch.set_data(frames[i])
anim = animation.FuncAnimation(plt.gcf(),
animate,
frames=len(frames),
interval=50)
anim.save(args.outdir + '/test.mp4')
return anim