def main():
import logging
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', type=int, default=0)
parser.add_argument('--env', type=str, default='Hopper-v1')
parser.add_argument('--arch',
type=str,
default='FFGaussian',
choices=('FFSoftmax', 'FFMellowmax', 'FFGaussian'))
parser.add_argument('--normalize-obs', action='store_true')
parser.add_argument('--bound-mean', action='store_true')
parser.add_argument('--seed', type=int, default=None)
parser.add_argument('--outdir', type=str, default=None)
parser.add_argument('--steps', type=int, default=10**6)
parser.add_argument('--eval-interval', type=int, default=10000)
parser.add_argument('--eval-n-runs', type=int, default=10)
parser.add_argument('--reward-scale-factor', type=float, default=1e-2)
parser.add_argument('--standardize-advantages', action='store_true')
parser.add_argument('--render', action='store_true', default=False)
parser.add_argument('--lr', type=float, default=3e-4)
parser.add_argument('--weight-decay', type=float, default=0.0)
parser.add_argument('--demo', action='store_true', default=False)
parser.add_argument('--load', type=str, default='')
parser.add_argument('--logger-level', type=int, default=logging.DEBUG)
parser.add_argument('--monitor', action='store_true')
parser.add_argument('--update-interval', type=int, default=2048)
parser.add_argument('--batchsize', type=int, default=64)
parser.add_argument('--epochs', type=int, default=10)
parser.add_argument('--entropy-coef', type=float, default=0.0)
args = parser.parse_args()
logging.getLogger().setLevel(args.logger_level)
if args.seed is not None:
misc.set_random_seed(args.seed)
args.outdir = experiments.prepare_output_dir(args, args.outdir)
def make_env(test):
env = gym.make(args.env)
if args.monitor:
env = gym.wrappers.Monitor(env, args.outdir)
# Scale rewards observed by agents
if args.reward_scale_factor and not test:
misc.env_modifiers.make_reward_filtered(
env, lambda x: x * args.reward_scale_factor)
if args.render:
misc.env_modifiers.make_rendered(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 args.arch == 'FFSoftmax':
model = A3CFFSoftmax(obs_space.low.size, action_space.n)
elif args.arch == 'FFMellowmax':
model = A3CFFMellowmax(obs_space.low.size, action_space.n)
elif args.arch == 'FFGaussian':
model = A3CFFGaussian(obs_space.low.size,
action_space,
bound_mean=args.bound_mean,
normalize_obs=args.normalize_obs)
opt = chainer.optimizers.Adam(alpha=args.lr, eps=1e-5)
opt.setup(model)
if args.weight_decay > 0:
opt.add_hook(NonbiasWeightDecay(args.weight_decay))
agent = PPO(
model,
opt,
gpu=args.gpu,
phi=phi,
update_interval=args.update_interval,
minibatch_size=args.batchsize,
epochs=args.epochs,
clip_eps_vf=None,
entropy_coef=args.entropy_coef,
standardize_advantages=args.standardize_advantages,
)
if args.load:
agent.load(args.load)
if args.demo:
env = make_env(True)
eval_stats = experiments.eval_performance(
env=env,
agent=agent,
n_runs=args.eval_n_runs,
max_episode_len=timestep_limit)
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.alpha = value
lr_decay_hook = experiments.LinearInterpolationHook(
args.steps, args.lr, 0, lr_setter)
# Linearly decay the clipping parameter to zero
def clip_eps_setter(env, agent, value):
agent.clip_eps = value
clip_eps_decay_hook = experiments.LinearInterpolationHook(
args.steps, 0.2, 0, clip_eps_setter)
experiments.train_agent_with_evaluation(
agent=agent,
env=make_env(False),
eval_env=make_env(True),
outdir=args.outdir,
steps=args.steps,
eval_n_runs=args.eval_n_runs,
eval_interval=args.eval_interval,
max_episode_len=timestep_limit,
step_hooks=[
lr_decay_hook,
clip_eps_decay_hook,
],
)
def main():
# Prevent numpy from using multiple threads
os.environ['OMP_NUM_THREADS'] = '1'
import logging
logging.basicConfig(level=logging.DEBUG)
parser = argparse.ArgumentParser()
parser.add_argument('rom', type=str)
parser.add_argument('--gpu', type=int, default=0)
parser.add_argument('--seed',
type=int,
default=0,
help='Random seed [0, 2 ** 31)')
parser.add_argument('--outdir', type=str, default=None)
parser.add_argument('--use-sdl', action='store_true')
parser.add_argument('--max-episode-len', type=int, default=10000)
parser.add_argument('--profile', action='store_true')
parser.add_argument('--steps', type=int, default=8 * 10**7)
parser.add_argument('--lr', type=float, default=2.5e-4)
parser.add_argument('--eval-interval', type=int, default=10**6)
parser.add_argument('--eval-n-runs', type=int, default=10)
parser.add_argument('--standardize-advantages', action='store_true')
parser.add_argument('--weight-decay', type=float, default=0.0)
parser.add_argument('--demo', action='store_true', default=False)
parser.add_argument('--load', type=str, default='')
# In the original paper, agent runs in 8 environments parallely
# and samples 128 steps per environment.
# Sample 128 * 8 steps, instead.
parser.add_argument('--update-interval', type=int, default=128 * 8)
parser.add_argument('--batchsize', type=int, default=32)
parser.add_argument('--epochs', type=int, default=3)
parser.set_defaults(use_sdl=False)
args = parser.parse_args()
# Set a random seed used in ChainerRL.
misc.set_random_seed(args.seed, gpus=(args.gpu, ))
args.outdir = experiments.prepare_output_dir(args, args.outdir)
print('Output files are saved in {}'.format(args.outdir))
n_actions = ale.ALE(args.rom).number_of_actions
model = A3CFF(n_actions)
opt = chainer.optimizers.Adam(alpha=args.lr)
opt.setup(model)
opt.add_hook(chainer.optimizer.GradientClipping(40))
if args.weight_decay > 0:
opt.add_hook(NonbiasWeightDecay(args.weight_decay))
agent = PPO(
model,
opt,
gpu=args.gpu,
phi=dqn_phi,
update_interval=args.update_interval,
minibatch_size=args.batchsize,
epochs=args.epochs,
clip_eps=0.1,
clip_eps_vf=None,
standardize_advantages=args.standardize_advantages,
)
if args.load:
agent.load(args.load)
def make_env(test):
# Use different random seeds for train and test envs
env_seed = 2**31 - 1 - args.seed if test else args.seed
env = ale.ALE(args.rom,
use_sdl=args.use_sdl,
treat_life_lost_as_terminal=not test,
seed=env_seed)
if not test:
misc.env_modifiers.make_reward_clipped(env, -1, 1)
return env
if args.demo:
env = make_env(True)
eval_stats = experiments.eval_performance(env=env,
agent=agent,
n_runs=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.alpha = value
lr_decay_hook = experiments.LinearInterpolationHook(
args.steps, args.lr, 0, lr_setter)
# Linearly decay the clipping parameter to zero
def clip_eps_setter(env, agent, value):
agent.clip_eps = value
clip_eps_decay_hook = experiments.LinearInterpolationHook(
args.steps, 0.1, 0, clip_eps_setter)
experiments.train_agent_with_evaluation(
agent=agent,
env=make_env(False),
eval_env=make_env(True),
outdir=args.outdir,
steps=args.steps,
eval_n_runs=args.eval_n_runs,
eval_interval=args.eval_interval,
max_episode_len=args.max_episode_len,
step_hooks=[
lr_decay_hook,
clip_eps_decay_hook,
],
)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--env',
type=str,
default='BreakoutNoFrameskip-v4',
help='Gym Env ID.')
parser.add_argument('--gpu',
type=int,
default=0,
help='GPU device ID. Set to -1 to use CPUs only.')
parser.add_argument('--num-envs',
type=int,
default=8,
help='Number of env instances run in parallel.')
parser.add_argument('--seed',
type=int,
default=0,
help='Random seed [0, 2 ** 32)')
parser.add_argument('--outdir',
type=str,
default='results',
help='Directory path to save output files.'
' If it does not exist, it will be created.')
parser.add_argument('--steps',
type=int,
default=10**7,
help='Total time steps for training.')
parser.add_argument(
'--max-frames',
type=int,
default=30 * 60 * 60, # 30 minutes with 60 fps
help='Maximum number of frames for each episode.')
parser.add_argument('--lr',
type=float,
default=2.5e-4,
help='Learning rate.')
parser.add_argument('--eval-interval',
type=int,
default=100000,
help='Interval (in timesteps) between evaluation'
' phases.')
parser.add_argument('--eval-n-runs',
type=int,
default=10,
help='Number of episodes ran in an evaluation phase.')
parser.add_argument('--demo',
action='store_true',
default=False,
help='Run demo episodes, not training.')
parser.add_argument('--load',
type=str,
default='',
help='Directory path to load a saved agent data from'
' if it is a non-empty string.')
parser.add_argument('--logging-level',
type=int,
default=20,
help='Logging level. 10:DEBUG, 20:INFO etc.')
parser.add_argument('--render',
action='store_true',
default=False,
help='Render env states in a GUI window.')
parser.add_argument('--monitor',
action='store_true',
default=False,
help='Monitor env. Videos and additional information'
' are saved as output files.')
parser.add_argument('--update-interval',
type=int,
default=128 * 8,
help='Interval (in timesteps) between PPO iterations.')
parser.add_argument('--batchsize',
type=int,
default=32 * 8,
help='Size of minibatch (in timesteps).')
parser.add_argument('--epochs',
type=int,
default=4,
help='Number of epochs used for each PPO iteration.')
parser.add_argument('--log-interval',
type=int,
default=10000,
help='Interval (in timesteps) of printing logs.')
parser.add_argument('--recurrent',
action='store_true',
default=False,
help='Use a recurrent model. See the code for the'
' model definition.')
parser.add_argument('--flicker',
action='store_true',
default=False,
help='Use so-called flickering Atari, where each'
' screen is blacked out with probability 0.5.')
parser.add_argument('--no-frame-stack',
action='store_true',
default=False,
help='Disable frame stacking so that the agent can'
' only see the current screen.')
parser.add_argument('--checkpoint-frequency',
type=int,
default=None,
help='Frequency at which agents are stored.')
args = parser.parse_args()
import logging
logging.basicConfig(level=args.logging_level)
# Set a random seed used in ChainerRL.
misc.set_random_seed(args.seed, gpus=(args.gpu, ))
# 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.num_envs) + args.seed * args.num_envs
assert process_seeds.max() < 2**32
args.outdir = experiments.prepare_output_dir(args, args.outdir)
print('Output files are saved in {}'.format(args.outdir))
def make_env(idx, test):
# Use different random seeds for train and test envs
process_seed = int(process_seeds[idx])
env_seed = 2**32 - 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,
flicker=args.flicker,
frame_stack=not args.no_frame_stack,
)
env.seed(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_batch_env(test):
return chainerrl.envs.MultiprocessVectorEnv([
(lambda: make_env(idx, test))
for idx, env in enumerate(range(args.num_envs))
])
sample_env = make_env(0, test=False)
print('Observation space', sample_env.observation_space)
print('Action space', sample_env.action_space)
n_actions = sample_env.action_space.n
winit_last = chainer.initializers.LeCunNormal(1e-2)
if args.recurrent:
model = chainerrl.links.StatelessRecurrentSequential(
L.Convolution2D(None, 32, 8, stride=4), F.relu,
L.Convolution2D(None, 64, 4, stride=2), F.relu,
L.Convolution2D(None, 64, 3, stride=1), F.relu,
L.Linear(None, 512), F.relu, L.NStepGRU(1, 512, 512, 0),
chainerrl.links.Branched(
chainer.Sequential(
L.Linear(None, n_actions, initialW=winit_last),
chainerrl.distribution.SoftmaxDistribution,
),
L.Linear(None, 1),
))
else:
model = chainer.Sequential(
L.Convolution2D(None, 32, 8, stride=4), F.relu,
L.Convolution2D(None, 64, 4, stride=2), F.relu,
L.Convolution2D(None, 64, 3, stride=1), F.relu,
L.Linear(None, 512), F.relu,
chainerrl.links.Branched(
chainer.Sequential(
L.Linear(None, n_actions, initialW=winit_last),
chainerrl.distribution.SoftmaxDistribution,
),
L.Linear(None, 1),
))
# Draw the computational graph and save it in the output directory.
fake_obss = np.zeros(sample_env.observation_space.shape,
dtype=np.float32)[None]
if args.recurrent:
fake_out, _ = model(fake_obss, None)
else:
fake_out = model(fake_obss)
chainerrl.misc.draw_computational_graph([fake_out],
os.path.join(args.outdir, 'model'))
opt = chainer.optimizers.Adam(alpha=args.lr, eps=1e-5)
opt.setup(model)
opt.add_hook(chainer.optimizer.GradientClipping(0.5))
def phi(x):
# Feature extractor
return np.asarray(x, dtype=np.float32) / 255
agent = PPO(
model,
opt,
gpu=args.gpu,
phi=phi,
update_interval=args.update_interval,
minibatch_size=args.batchsize,
epochs=args.epochs,
clip_eps=0.1,
clip_eps_vf=None,
standardize_advantages=True,
entropy_coef=1e-2,
recurrent=args.recurrent,
)
if args.load:
agent.load(args.load)
if args.demo:
eval_stats = experiments.eval_performance(
env=make_batch_env(test=True),
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:
step_hooks = []
# Linearly decay the learning rate to zero
def lr_setter(env, agent, value):
agent.optimizer.alpha = value
step_hooks.append(
experiments.LinearInterpolationHook(args.steps, args.lr, 0,
lr_setter))
experiments.train_agent_batch_with_evaluation(
agent=agent,
env=make_batch_env(False),
eval_env=make_batch_env(True),
outdir=args.outdir,
steps=args.steps,
eval_n_steps=None,
eval_n_episodes=args.eval_n_runs,
checkpoint_freq=args.checkpoint_frequency,
eval_interval=args.eval_interval,
log_interval=args.log_interval,
save_best_so_far_agent=False,
step_hooks=step_hooks,
)
def main():
import logging
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', type=int, default=0,
help='GPU to use, set to -1 if no GPU.')
parser.add_argument('--env', type=str, default='Hopper-v2',
help='OpenAI Gym MuJoCo env to perform algorithm on.')
parser.add_argument('--num-envs', type=int, default=1,
help='Number of envs run in parallel.')
parser.add_argument('--seed', type=int, default=0,
help='Random seed [0, 2 ** 32)')
parser.add_argument('--outdir', type=str, default='results',
help='Directory path to save output files.'
' If it does not exist, it will be created.')
parser.add_argument('--steps', type=int, default=2 * 10 ** 6,
help='Total number of timesteps to train the agent.')
parser.add_argument('--eval-interval', type=int, default=100000,
help='Interval in timesteps between evaluations.')
parser.add_argument('--eval-n-runs', type=int, default=100,
help='Number of episodes run for each evaluation.')
parser.add_argument('--render', action='store_true',
help='Render env states in a GUI window.')
parser.add_argument('--demo', action='store_true',
help='Just run evaluation, not training.')
parser.add_argument('--load', type=str, default='',
help='Directory to load agent from.')
parser.add_argument('--logger-level', type=int, default=logging.INFO,
help='Level of the root logger.')
parser.add_argument('--monitor', action='store_true',
help='Wrap env with gym.wrappers.Monitor.')
parser.add_argument('--log-interval', type=int, default=1000,
help='Interval in timesteps between outputting log'
' messages during training')
parser.add_argument('--update-interval', type=int, default=2048,
help='Interval in timesteps between model updates.')
parser.add_argument('--epochs', type=int, default=10,
help='Number of epochs to update model for per PPO'
' iteration.')
parser.add_argument('--batch-size', type=int, default=64,
help='Minibatch size')
args = parser.parse_args()
logging.basicConfig(level=args.logger_level)
# Set a random seed used in ChainerRL
misc.set_random_seed(args.seed, gpus=(args.gpu,))
# 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.num_envs) + args.seed * args.num_envs
assert process_seeds.max() < 2 ** 32
args.outdir = experiments.prepare_output_dir(args, args.outdir)
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:
env = gym.wrappers.Monitor(env, args.outdir)
if args.render:
env = chainerrl.wrappers.Render(env)
return env
def make_batch_env(test):
return chainerrl.envs.MultiprocessVectorEnv(
[functools.partial(make_env, idx, test)
for idx, env in enumerate(range(args.num_envs))])
# Only for getting timesteps, and obs-action spaces
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
print('Observation space:', obs_space)
print('Action space:', action_space)
assert isinstance(action_space, gym.spaces.Box)
# Normalize observations based on their empirical mean and variance
obs_normalizer = chainerrl.links.EmpiricalNormalization(
obs_space.low.size, clip_threshold=5)
# While the original paper initialized weights by normal distribution,
# we use orthogonal initialization as the latest openai/baselines does.
winit = chainerrl.initializers.Orthogonal(1.)
winit_last = chainerrl.initializers.Orthogonal(1e-2)
action_size = action_space.low.size
policy = chainer.Sequential(
L.Linear(None, 64, initialW=winit),
F.tanh,
L.Linear(None, 64, initialW=winit),
F.tanh,
L.Linear(None, action_size, initialW=winit_last),
chainerrl.policies.GaussianHeadWithStateIndependentCovariance(
action_size=action_size,
var_type='diagonal',
var_func=lambda x: F.exp(2 * x), # Parameterize log std
var_param_init=0, # log std = 0 => std = 1
),
)
vf = chainer.Sequential(
L.Linear(None, 64, initialW=winit),
F.tanh,
L.Linear(None, 64, initialW=winit),
F.tanh,
L.Linear(None, 1, initialW=winit),
)
# Combine a policy and a value function into a single model
model = chainerrl.links.Branched(policy, vf)
opt = chainer.optimizers.Adam(3e-4, eps=1e-5)
opt.setup(model)
agent = PPO(
model,
opt,
obs_normalizer=obs_normalizer,
gpu=args.gpu,
update_interval=args.update_interval,
minibatch_size=args.batch_size,
epochs=args.epochs,
clip_eps_vf=None,
entropy_coef=0,
standardize_advantages=True,
gamma=0.995,
lambd=0.97,
)
if args.load:
agent.load(args.load)
if args.demo:
env = make_batch_env(True)
eval_stats = experiments.eval_performance(
env=env,
agent=agent,
n_steps=None,
n_episodes=args.eval_n_runs,
max_episode_len=timestep_limit)
print('n_runs: {} mean: {} median: {} stdev {}'.format(
args.eval_n_runs, eval_stats['mean'], eval_stats['median'],
eval_stats['stdev']))
else:
experiments.train_agent_batch_with_evaluation(
agent=agent,
env=make_batch_env(False),
eval_env=make_batch_env(True),
outdir=args.outdir,
steps=args.steps,
eval_n_steps=None,
eval_n_episodes=args.eval_n_runs,
eval_interval=args.eval_interval,
log_interval=args.log_interval,
max_episode_len=timestep_limit,
save_best_so_far_agent=False,
)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--env', type=str, default='BreakoutNoFrameskip-v4')
parser.add_argument('--gpu', type=int, default=0)
parser.add_argument('--seed',
type=int,
default=0,
help='Random seed [0, 2 ** 31)')
parser.add_argument('--outdir',
type=str,
default='results',
help='Directory path to save output files.'
' If it does not exist, it will be created.')
parser.add_argument('--steps', type=int, default=10**7)
parser.add_argument(
'--max-episode-len',
type=int,
default=5 * 60 * 60 // 4, # 5 minutes with 60/4 fps
help='Maximum number of steps for each episode.')
parser.add_argument('--lr', type=float, default=2.5e-4)
parser.add_argument('--eval-interval', type=int, default=10**5)
parser.add_argument('--eval-n-runs', type=int, default=10)
parser.add_argument('--standardize-advantages', action='store_true')
parser.add_argument('--weight-decay', type=float, default=0.0)
parser.add_argument('--demo', action='store_true', default=False)
parser.add_argument('--load', type=str, default='')
parser.add_argument('--logging-level',
type=int,
default=20,
help='Logging level. 10:DEBUG, 20:INFO etc.')
parser.add_argument('--render',
action='store_true',
default=False,
help='Render env states in a GUI window.')
parser.add_argument('--monitor',
action='store_true',
default=False,
help='Monitor env. Videos and additional information'
' are saved as output files.')
# In the original paper, agent runs in 8 environments parallely
# and samples 128 steps per environment.
# Sample 128 * 8 steps, instead.
parser.add_argument('--update-interval', type=int, default=128 * 8)
parser.add_argument('--batchsize', type=int, default=32)
parser.add_argument('--epochs', type=int, default=3)
args = parser.parse_args()
import logging
logging.basicConfig(level=args.logging_level)
# Set a random seed used in ChainerRL.
misc.set_random_seed(args.seed, gpus=(args.gpu, ))
# Set different random seeds for train and test envs.
train_seed = args.seed
test_seed = 2**31 - 1 - args.seed
args.outdir = experiments.prepare_output_dir(args, args.outdir)
print('Output files are saved in {}'.format(args.outdir))
def make_env(test):
# Use different random seeds for train and test envs
env_seed = test_seed if test else train_seed
env = atari_wrappers.wrap_deepmind(atari_wrappers.make_atari(args.env),
episode_life=not test,
clip_rewards=not test)
env.seed(int(env_seed))
if args.monitor:
env = gym.wrappers.Monitor(
env, args.outdir, mode='evaluation' if test else 'training')
if args.render:
env = chainerrl.wrappers.Render(env)
return env
env = make_env(test=False)
eval_env = make_env(test=True)
n_actions = env.action_space.n
model = A3CFF(n_actions)
opt = chainer.optimizers.Adam(alpha=args.lr)
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 = PPO(
model,
opt,
gpu=args.gpu,
phi=phi,
update_interval=args.update_interval,
minibatch_size=args.batchsize,
epochs=args.epochs,
clip_eps=0.1,
clip_eps_vf=None,
standardize_advantages=args.standardize_advantages,
)
if args.load:
agent.load(args.load)
if args.demo:
eval_stats = experiments.eval_performance(env=eval_env,
agent=agent,
n_runs=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.alpha = value
lr_decay_hook = experiments.LinearInterpolationHook(
args.steps, args.lr, 0, lr_setter)
# Linearly decay the clipping parameter to zero
def clip_eps_setter(env, agent, value):
agent.clip_eps = max(value, 1e-8)
clip_eps_decay_hook = experiments.LinearInterpolationHook(
args.steps, 0.1, 0, clip_eps_setter)
experiments.train_agent_with_evaluation(
agent=agent,
env=env,
eval_env=eval_env,
outdir=args.outdir,
steps=args.steps,
eval_n_episodes=args.eval_n_runs,
eval_interval=args.eval_interval,
train_max_episode_len=args.max_episode_len,
save_best_so_far_agent=False,
step_hooks=[
lr_decay_hook,
clip_eps_decay_hook,
],
)
def main():
import logging
parser = argparse.ArgumentParser()
parser.add_argument('algo', default='ppo', choices=['ppo', 'gail', 'airl'], type=str)
parser.add_argument('--gpu', type=int, default=0)
parser.add_argument('--env', type=str, default='Hopper-v2')
parser.add_argument('--arch', type=str, default='FFGaussian',
choices=('FFSoftmax', 'FFMellowmax',
'FFGaussian'))
parser.add_argument('--bound-mean', action='store_true')
parser.add_argument('--seed', type=int, default=0,
help='Random seed [0, 2 ** 32)')
parser.add_argument('--outdir', type=str, default='results',
help='Directory path to save output files.'
' If it does not exist, it will be created.')
parser.add_argument('--steps', type=int, default=10 ** 6)
parser.add_argument('--eval-interval', type=int, default=10000)
parser.add_argument('--eval-n-runs', type=int, default=10)
parser.add_argument('--reward-scale-factor', type=float, default=1e-2)
parser.add_argument('--standardize-advantages', action='store_true')
parser.add_argument('--render', action='store_true', default=False)
parser.add_argument('--lr', type=float, default=3e-4)
parser.add_argument('--weight-decay', type=float, default=0.0)
parser.add_argument('--demo', action='store_true', default=False)
parser.add_argument('--load', type=str, default='')
parser.add_argument('--load_demo', type=str, default='')
parser.add_argument('--logger-level', type=int, default=logging.DEBUG)
parser.add_argument('--monitor', action='store_true')
parser.add_argument('--update-interval', type=int, default=2048)
parser.add_argument('--batchsize', type=int, default=64)
parser.add_argument('--epochs', type=int, default=10)
parser.add_argument('--entropy-coef', type=float, default=0.0)
args = parser.parse_args()
logging.basicConfig(level=args.logger_level)
# Set a random seed used in ChainerRL
misc.set_random_seed(args.seed, gpus=(args.gpu,))
if not (args.demo and args.load):
args.outdir = experiments.prepare_output_dir(args, args.outdir)
def make_env(test):
env = gym.make(args.env)
# Use different random seeds for train and test envs
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:
env = gym.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:
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
# Normalize observations based on their empirical mean and variance
obs_normalizer = chainerrl.links.EmpiricalNormalization(
obs_space.low.size, clip_threshold=5)
# Switch policy types accordingly to action space types
if args.arch == 'FFSoftmax':
model = A3CFFSoftmax(obs_space.low.size, action_space.n)
elif args.arch == 'FFMellowmax':
model = A3CFFMellowmax(obs_space.low.size, action_space.n)
elif args.arch == 'FFGaussian':
model = A3CFFGaussian(obs_space.low.size, action_space,
bound_mean=args.bound_mean)
opt = chainer.optimizers.Adam(alpha=args.lr, eps=1e-5)
opt.setup(model)
if args.weight_decay > 0:
opt.add_hook(NonbiasWeightDecay(args.weight_decay))
if args.algo == 'ppo':
agent = PPO(model, opt,
obs_normalizer=obs_normalizer,
gpu=args.gpu,
update_interval=args.update_interval,
minibatch_size=args.batchsize, epochs=args.epochs,
clip_eps_vf=None, entropy_coef=args.entropy_coef,
standardize_advantages=args.standardize_advantages,
)
elif args.algo == 'gail':
import numpy as np
from irl.gail import GAIL
from irl.gail import Discriminator
demonstrations = np.load(args.load_demo)
D = Discriminator(gpu=args.gpu)
agent = GAIL(demonstrations=demonstrations, discriminator=D,
model=model, optimizer=opt,
obs_normalizer=obs_normalizer,
gpu=args.gpu,
update_interval=args.update_interval,
minibatch_size=args.batchsize, epochs=args.epochs,
clip_eps_vf=None, entropy_coef=args.entropy_coef,
standardize_advantages=args.standardize_advantages,)
elif args.algo == 'airl':
import numpy as np
from irl.airl import AIRL as Agent
from irl.airl import Discriminator
# obs_normalizer = None
demonstrations = np.load(args.load_demo)
D = Discriminator(gpu=args.gpu)
agent = Agent(demonstrations=demonstrations, discriminator=D,
model=model, optimizer=opt,
obs_normalizer=obs_normalizer,
gpu=args.gpu,
update_interval=args.update_interval,
minibatch_size=args.batchsize, epochs=args.epochs,
clip_eps_vf=None, entropy_coef=args.entropy_coef,
standardize_advantages=args.standardize_advantages,)
if args.load:
agent.load(args.load)
if args.demo:
env = make_env(True)
eval_stats = experiments.eval_performance(
env=env,
agent=agent,
n_steps=None,
n_episodes=args.eval_n_runs,
max_episode_len=timestep_limit)
print('n_runs: {} mean: {} median: {} stdev {}'.format(
args.eval_n_runs, eval_stats['mean'], eval_stats['median'],
eval_stats['stdev']))
outdir = args.load if args.load else args.outdir
save_agent_demo(make_env(False), agent, outdir)
else:
# Linearly decay the learning rate to zero
def lr_setter(env, agent, value):
agent.optimizer.alpha = value
lr_decay_hook = experiments.LinearInterpolationHook(
args.steps, args.lr, 0, lr_setter)
# Linearly decay the clipping parameter to zero
def clip_eps_setter(env, agent, value):
agent.clip_eps = max(value, 1e-8)
clip_eps_decay_hook = experiments.LinearInterpolationHook(
args.steps, 0.2, 0, clip_eps_setter)
experiments.train_agent_with_evaluation(
agent=agent,
env=make_env(False),
eval_env=make_env(True),
outdir=args.outdir,
steps=args.steps,
eval_n_steps=None,
eval_n_episodes=args.eval_n_runs,
eval_interval=args.eval_interval,
train_max_episode_len=timestep_limit,
save_best_so_far_agent=False,
step_hooks=[
lr_decay_hook,
clip_eps_decay_hook,
],
)
save_agent_demo(make_env(False), agent, args.outdir)
def __init__(self, args, sample_env):
obs_space = sample_env.observation_space
action_space = sample_env.action_space
# Normalize observations based on their empirical mean and variance
obs_normalizer = chainerrl.links.EmpiricalNormalization(
obs_space.low.size, clip_threshold=5)
# Switch policy types accordingly to action space types
if args.arch == 'FFSoftmax':
#model = A3CFFSoftmax(obs_space.low.size, action_space.n)
model = A3CFFSoftmax(obs_space.low.size,
sample_env.env_prop.get_softmax_layer_size(),
n_hidden_channels=600,
beta=cfg.beta)
elif args.arch == 'FFMellowmax':
model = A3CFFMellowmax(obs_space.low.size, action_space.n)
elif args.arch == 'FFGaussian':
model = A3CFFGaussian(obs_space.low.size,
action_space,
bound_mean=args.bound_mean,
n_hidden_channels=cfg.n_hidden_channels)
elif args.arch == 'FFParamSoftmax':
model = A3CFFParamSoftmax(
obs_space.low.size,
sample_env.env_prop.get_pre_output_layer_size(),
sample_env.env_prop.get_parametric_segments(),
sample_env.env_prop.get_parametric_softmax_segments_sizes(),
n_hidden_channels=600,
beta=cfg.beta)
else:
raise NotImplementedError
opt = chainer.optimizers.Adam(alpha=args.adam_lr, eps=1e-5)
opt.setup(model)
if args.weight_decay > 0:
opt.add_hook(NonbiasWeightDecay(args.weight_decay))
# a workaround for saving obs_normalizer
# see https://github.com/chainer/chainerrl/issues/376
if 'obs_normalizer' not in PPO.saved_attributes:
PPO.saved_attributes.append('obs_normalizer')
agent = PPO(
model,
opt,
obs_normalizer=obs_normalizer,
gpu=args.gpu,
phi=lambda x: x.astype(np.float32, copy=False),
gamma=args.ppo_gamma,
lambd=args.ppo_lambda,
update_interval=args.ppo_update_interval,
minibatch_size=args.batchsize,
epochs=args.epochs,
clip_eps_vf=None,
entropy_coef=args.entropy_coef,
standardize_advantages=args.standardize_advantages,
)
if args.load:
agent.load(args.load)
self._agent = agent
def main():
import logging
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', type=int, default=0)
parser.add_argument('--env', type=str, default='Hopper-v2')
parser.add_argument('--num-envs', type=int, default=1)
parser.add_argument('--arch',
type=str,
default='FFGaussian',
choices=('FFSoftmax', 'FFMellowmax', 'FFGaussian'))
parser.add_argument('--bound-mean', action='store_true')
parser.add_argument('--seed',
type=int,
default=0,
help='Random seed [0, 2 ** 32)')
parser.add_argument('--outdir',
type=str,
default='results',
help='Directory path to save output files.'
' If it does not exist, it will be created.')
parser.add_argument('--steps', type=int, default=10**6)
parser.add_argument('--eval-interval', type=int, default=10000)
parser.add_argument('--eval-n-runs', type=int, default=10)
parser.add_argument('--reward-scale-factor', type=float, default=1e-2)
parser.add_argument('--standardize-advantages', action='store_true')
parser.add_argument('--render', action='store_true', default=False)
parser.add_argument('--lr', type=float, default=3e-4)
parser.add_argument('--weight-decay', type=float, default=0.0)
parser.add_argument('--load', type=str, default='')
parser.add_argument('--logger-level', type=int, default=logging.DEBUG)
parser.add_argument('--monitor', action='store_true')
parser.add_argument('--window-size', type=int, default=100)
parser.add_argument('--update-interval', type=int, default=2048)
parser.add_argument('--log-interval', type=int, default=1000)
parser.add_argument('--batchsize', type=int, default=64)
parser.add_argument('--epochs', type=int, default=10)
parser.add_argument('--entropy-coef', type=float, default=0.0)
args = parser.parse_args()
#logging.basicConfig(level=args.logger_level)
# Set a random seed used in ChainerRL
misc.set_random_seed(args.seed, gpus=(args.gpu, ))
# 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.num_envs) + args.seed * args.num_envs
assert process_seeds.max() < 2**32
args.outdir = experiments.prepare_output_dir(args, args.outdir)
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:
env = gym.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:
env = chainerrl.wrappers.Render(env)
return env
def make_batch_env(test):
return chainerrl.envs.MultiprocessVectorEnv([
(lambda: make_env(idx, test))
for idx, env in enumerate(range(args.num_envs))
])
# Only for getting timesteps, and obs-action spaces
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
# Normalize observations based on their empirical mean and variance
obs_normalizer = chainerrl.links.EmpiricalNormalization(obs_space.low.size,
clip_threshold=5)
# Switch policy types accordingly to action space types
if args.arch == 'FFSoftmax':
model = A3CFFSoftmax(obs_space.low.size, action_space.n)
elif args.arch == 'FFMellowmax':
model = A3CFFMellowmax(obs_space.low.size, action_space.n)
elif args.arch == 'FFGaussian':
model = A3CFFGaussian(obs_space.low.size,
action_space,
bound_mean=args.bound_mean)
opt = chainer.optimizers.Adam(alpha=args.lr, eps=1e-5)
opt.setup(model)
if args.weight_decay > 0:
opt.add_hook(NonbiasWeightDecay(args.weight_decay))
agent = PPO(
model,
opt,
obs_normalizer=obs_normalizer,
gpu=args.gpu,
update_interval=args.ppo_update_interval,
minibatch_size=args.batchsize,
epochs=args.epochs,
clip_eps_vf=None,
entropy_coef=args.entropy_coef,
standardize_advantages=args.standardize_advantages,
)
if args.load:
agent.load(args.load)
# Linearly decay the learning rate to zero
def lr_setter(env, agent, value):
agent.optimizer.alpha = value
lr_decay_hook = experiments.LinearInterpolationHook(
args.steps, args.lr, 0, lr_setter)
# Linearly decay the clipping parameter to zero
def clip_eps_setter(env, agent, value):
agent.clip_eps = value
clip_eps_decay_hook = experiments.LinearInterpolationHook(
args.steps, 0.2, 0, clip_eps_setter)
experiments.train_agent_batch_with_evaluation(
agent=agent,
env=make_batch_env(False),
eval_env=make_batch_env(True),
outdir=args.outdir,
steps=args.steps,
eval_n_runs=args.eval_n_runs,
eval_interval=args.eval_interval,
log_interval=args.log_interval,
return_window_size=args.window_size,
max_episode_len=timestep_limit,
save_best_so_far_agent=False,
step_hooks=[
lr_decay_hook,
clip_eps_decay_hook,
],
)
action_space = env.action_space
model = A3CFFGaussian(obs_space.low.size,
action_space,
bound_mean=False,
normalize_obs=False)
opt = chainer.optimizers.Adam(alpha=3e-4, eps=1e-5)
opt.setup(model)
agent = PPO(model,
opt,
gpu=-1,
phi=phi,
update_interval=2048,
minibatch_size=64,
epochs=10,
clip_eps_vf=None,
entropy_coef=0.0,
standardize_advantages=False)
agent.load("parameters")
ACTION_MEANINGS = {
0: 'Hip1(Torque/Velocity)',
1: 'Knee1(Torque/Velocity)',
2: 'Hip2(Torque/Velocity)',
3: 'Knee2(Torque/Velocity)',
}
launch_visualizer(agent, env, ACTION_MEANINGS)
def main():
import logging
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', type=int, default=0)
parser.add_argument('--env', type=str, default='Hopper-v2')
parser.add_argument('--num-envs', type=int, default=1)
parser.add_argument('--seed',
type=int,
default=0,
help='Random seed [0, 2 ** 32)')
parser.add_argument('--outdir',
type=str,
default='results',
help='Directory path to save output files.'
' If it does not exist, it will be created.')
parser.add_argument('--steps', type=int, default=10**6)
parser.add_argument('--eval-interval', type=int, default=10000)
parser.add_argument('--eval-n-runs', type=int, default=10)
parser.add_argument('--reward-scale-factor', type=float, default=1e-2)
parser.add_argument('--standardize-advantages', action='store_true')
parser.add_argument('--render', action='store_true', default=False)
parser.add_argument('--lr', type=float, default=3e-4)
parser.add_argument('--weight-decay', type=float, default=0.0)
parser.add_argument('--demo', action='store_true', default=False)
parser.add_argument('--load', type=str, default='')
parser.add_argument('--logger-level', type=int, default=logging.DEBUG)
parser.add_argument('--monitor', action='store_true')
parser.add_argument('--window-size', type=int, default=100)
parser.add_argument('--update-interval', type=int, default=2048)
parser.add_argument('--log-interval', type=int, default=1000)
parser.add_argument('--batchsize', type=int, default=64)
parser.add_argument('--epochs', type=int, default=10)
parser.add_argument('--entropy-coef', type=float, default=0.0)
args = parser.parse_args()
logging.basicConfig(level=args.logger_level)
# Set a random seed used in ChainerRL
misc.set_random_seed(args.seed, gpus=(args.gpu, ))
# 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.num_envs) + args.seed * args.num_envs
assert process_seeds.max() < 2**32
args.outdir = experiments.prepare_output_dir(args, args.outdir)
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:
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:
env = chainerrl.wrappers.Render(env)
return env
def make_batch_env(test):
return chainerrl.envs.MultiprocessVectorEnv([
functools.partial(make_env, idx, test)
for idx, env in enumerate(range(args.num_envs))
])
# Only for getting timesteps, and obs-action spaces
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
# Normalize observations based on their empirical mean and variance
obs_normalizer = chainerrl.links.EmpiricalNormalization(obs_space.low.size,
clip_threshold=5)
winit_last = chainer.initializers.LeCunNormal(1e-2)
# Switch policy types accordingly to action space types
if isinstance(action_space, gym.spaces.Discrete):
n_actions = action_space.n
policy = chainer.Sequential(
L.Linear(None, 64),
F.tanh,
L.Linear(None, 64),
F.tanh,
L.Linear(None, n_actions, initialW=winit_last),
chainerrl.distribution.SoftmaxDistribution,
)
elif isinstance(action_space, gym.spaces.Box):
action_size = action_space.low.size
policy = chainer.Sequential(
L.Linear(None, 64),
F.tanh,
L.Linear(None, 64),
F.tanh,
L.Linear(None, action_size, initialW=winit_last),
chainerrl.policies.GaussianHeadWithStateIndependentCovariance(
action_size=action_size,
var_type='diagonal',
var_func=lambda x: F.exp(2 * x), # Parameterize log std
var_param_init=0, # log std = 0 => std = 1
),
)
else:
print("""\
This example only supports gym.spaces.Box or gym.spaces.Discrete action spaces."""
) # NOQA
return
vf = chainer.Sequential(
L.Linear(None, 64),
F.tanh,
L.Linear(None, 64),
F.tanh,
L.Linear(None, 1),
)
# Combine a policy and a value function into a single model
model = chainerrl.links.Branched(policy, vf)
opt = chainer.optimizers.Adam(alpha=args.lr, eps=1e-5)
opt.setup(model)
if args.weight_decay > 0:
opt.add_hook(NonbiasWeightDecay(args.weight_decay))
agent = PPO(
model,
opt,
obs_normalizer=obs_normalizer,
gpu=args.gpu,
update_interval=args.update_interval,
minibatch_size=args.batchsize,
epochs=args.epochs,
clip_eps_vf=None,
entropy_coef=args.entropy_coef,
standardize_advantages=args.standardize_advantages,
)
if args.load:
agent.load(args.load)
if args.demo:
env = make_batch_env(True)
eval_stats = experiments.eval_performance(
env=env,
agent=agent,
n_steps=None,
n_episodes=args.eval_n_runs,
max_episode_len=timestep_limit)
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.alpha = value
lr_decay_hook = experiments.LinearInterpolationHook(
args.steps, args.lr, 0, lr_setter)
experiments.train_agent_batch_with_evaluation(
agent=agent,
env=make_batch_env(False),
eval_env=make_batch_env(True),
outdir=args.outdir,
steps=args.steps,
eval_n_steps=None,
eval_n_episodes=args.eval_n_runs,
eval_interval=args.eval_interval,
log_interval=args.log_interval,
return_window_size=args.window_size,
max_episode_len=timestep_limit,
save_best_so_far_agent=False,
step_hooks=[
lr_decay_hook,
],
)
def main():
import logging
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', type=int, default=-1)
parser.add_argument('--env', type=str, default='Hopper-v2')
parser.add_argument('--num-envs', type=int, default=1)
parser.add_argument('--seed',
type=int,
default=0,
help='Random seed [0, 2 ** 32)')
parser.add_argument('--outdir',
type=str,
default='results',
help='Directory path to save output files.'
' If it does not exist, it will be created.')
parser.add_argument('--steps', type=int, default=10**6)
parser.add_argument('--eval-interval', type=int, default=10000)
parser.add_argument('--eval-n-runs', type=int, default=10)
parser.add_argument('--reward-scale-factor', type=float, default=1e-2)
parser.add_argument('--standardize-advantages', action='store_true')
parser.add_argument('--render', action='store_true', default=False)
parser.add_argument('--lr', type=float, default=3e-4)
parser.add_argument('--weight-decay', type=float, default=0.0)
parser.add_argument('--demo', action='store_true', default=False)
parser.add_argument('--load', type=str, default='')
parser.add_argument('--logger-level', type=int, default=logging.DEBUG)
parser.add_argument('--monitor', action='store_true')
parser.add_argument('--window-size', type=int, default=100)
parser.add_argument('--update-interval', type=int, default=2048)
parser.add_argument('--log-interval', type=int, default=1000)
parser.add_argument('--batchsize', type=int, default=64)
parser.add_argument('--epochs', type=int, default=10)
parser.add_argument('--entropy-coef', type=float, default=0.0)
args = parser.parse_args()
logging.basicConfig(level=args.logger_level)
# Set a random seed used in ChainerRL
misc.set_random_seed(args.seed, gpus=(args.gpu, ))
# 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.num_envs) + args.seed * args.num_envs
assert process_seeds.max() < 2**32
args.outdir = experiments.prepare_output_dir(args, args.outdir)
# 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:
# 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:
# env = chainerrl.wrappers.Render(env)
# return env
def make_env(test):
env = gym.make(
"DaktyPushingSimulationEnv-v0",
level=5,
simulation_backend="mujoco",
control_frequency_in_hertz=100,
state_space_components_to_be_used=None,
alternate_env_object=None,
discretization_factor_torque_control_space=None,
model_as_function_for_pixel_to_latent_space_parsing=(None, None))
# print('\n############\n', env, '\n############\n')
env.unwrapped.finger.set_resolution_quality('low')
# print('\n############\n', env, '\n############\n')
env = gym.wrappers.TimeLimit(env)
# print('\n############\n', env, '\n############\n')
# Unwrap TimeLimit wrapper
assert isinstance(env, gym.wrappers.TimeLimit)
env = env.env
# Use different random seeds for train and test envs
# env_seed = 2 ** 32 - 1 - args.seed if test else args.seed
# env.seed(env_seed)
process_seed = 420
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:
env = chainerrl.wrappers.Monitor(env, args.outdir)
if args.render and not test:
env = chainerrl.wrappers.Render(env)
return env
def make_batch_env(test):
return chainerrl.envs.MultiprocessVectorEnv([
functools.partial(make_env, idx, test)
for idx, env in enumerate(range(args.num_envs))
])
# Only for getting timesteps, and obs-action spaces
sample_env = make_env(0)
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
print('\n\n------------------- obs_space: ', obs_space.shape, '\n\n\n')
# Normalize observations based on their empirical mean and variance
obs_normalizer = chainerrl.links.EmpiricalNormalization(obs_space.low.size,
clip_threshold=5)
winit_last = chainer.initializers.LeCunNormal(1e-2)
action_size = action_space.low.size
policy = chainer.Sequential(
L.Linear(None, 64),
F.tanh,
L.Linear(None, 64),
F.tanh,
L.Linear(None, action_size, initialW=winit_last),
chainerrl.policies.GaussianHeadWithStateIndependentCovariance(
action_size=action_size,
var_type='diagonal',
var_func=lambda x: F.exp(2 * x), # Parameterize log std
var_param_init=0, # log std = 0 => std = 1
))
vf = chainer.Sequential(
concat_obs_and_action,
L.Linear(None, 64),
F.tanh,
L.Linear(None, 64),
F.tanh,
L.Linear(None, 1),
)
# Combine a policy and a value function into a single model
model = chainerrl.links.Branched(policy, vf)
opt = chainer.optimizers.Adam(alpha=args.lr, eps=1e-5)
opt.setup(model)
if args.weight_decay > 0:
opt.add_hook(NonbiasWeightDecay(args.weight_decay))
agent = PPO(
model,
opt,
obs_normalizer=obs_normalizer,
gpu=args.gpu,
update_interval=args.update_interval,
minibatch_size=args.batchsize,
epochs=args.epochs,
clip_eps_vf=None,
entropy_coef=args.entropy_coef,
standardize_advantages=args.standardize_advantages,
)
if args.load:
agent.load(args.load)
if args.demo:
env = make_env(True)
eval_stats = experiments.eval_performance(
env=env,
agent=agent,
n_steps=None,
n_episodes=args.eval_n_runs,
max_episode_len=timestep_limit)
print('n_runs: {} mean: {} median: {} stdev {}'.format(
args.eval_n_runs, eval_stats['mean'], eval_stats['median'],
eval_stats['stdev']))
else:
env = make_env(False)
n_episodes = 10000
# pbar = tqdm(total=n_episodes)
max_episode_len = 1000
for i in range(1, n_episodes + 1):
# pbar.update(1)
obs = env.reset()
# print('obs inital..............', obs.shape)
reward = 0
done = False
R = 0 # return (sum of rewards)
t = 0 # time step
# pbar = tqdm(total=max_episode_len)
while not done and t < max_episode_len:
# pbar.update(1)
# Uncomment to watch the behaviour
# env.render()
action = agent.act_and_train(obs, reward)
# print('action..................', action)
obs, reward, done, _ = env.step(action)
# print('obs.....................', obs)
# print('reward..................', reward)
R += reward
t += 1
if i % 10 == 0:
print('episode:', i, 'R:', R, 'statistics:',
agent.get_statistics())
agent.stop_episode_and_train(obs, reward, done)
print('Finished.')
# Linearly decay the learning rate to zero
def lr_setter(env, agent, value):
agent.optimizer.alpha = value
lr_decay_hook = experiments.LinearInterpolationHook(
args.steps, args.lr, 0, lr_setter)
experiments.train_agent_batch_with_evaluation(
agent=agent,
env=make_env(False),
eval_env=make_env(True),
outdir=args.outdir,
steps=args.steps,
eval_n_steps=None,
eval_n_episodes=args.eval_n_runs,
eval_interval=args.eval_interval,
log_interval=args.log_interval,
return_window_size=args.window_size,
max_episode_len=timestep_limit,
save_best_so_far_agent=False,
step_hooks=[
lr_decay_hook,
],
)
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.
misc.set_random_seed(args.seed, gpus=(args.gpu,))
# 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.num_envs) + args.seed * args.num_envs
assert process_seeds.max() < 2 ** 32
def make_env(idx, test):
# Use different random seeds for train and test envs
process_seed = int(process_seeds[idx])
env_seed = 2 ** 32 - 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,
flicker=args.flicker,
frame_stack=not args.no_frame_stack,
)
env.seed(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
def make_batch_env(test):
return chainerrl.envs.MultiprocessVectorEnv(
[(lambda: make_env(idx, test))
for idx, env in enumerate(range(args.num_envs))])
sample_env = make_env(0, test=False)
print('Observation space', sample_env.observation_space)
print('Action space', sample_env.action_space)
n_actions = sample_env.action_space.n
winit_last = chainer.initializers.LeCunNormal(1e-2)
if args.recurrent:
model = chainerrl.links.StatelessRecurrentSequential(
L.Convolution2D(None, 32, 8, stride=4),
F.relu,
L.Convolution2D(None, 64, 4, stride=2),
F.relu,
L.Convolution2D(None, 64, 3, stride=1),
F.relu,
L.Linear(None, 512),
F.relu,
L.NStepGRU(1, 512, 512, 0),
chainerrl.links.Branched(
chainer.Sequential(
L.Linear(None, n_actions, initialW=winit_last),
chainerrl.distribution.SoftmaxDistribution,
),
L.Linear(None, 1),
)
)
else:
model = chainer.Sequential(
L.Convolution2D(None, 32, 8, stride=4),
F.relu,
L.Convolution2D(None, 64, 4, stride=2),
F.relu,
L.Convolution2D(None, 64, 3, stride=1),
F.relu,
L.Linear(None, 512),
F.relu,
chainerrl.links.Branched(
chainer.Sequential(
L.Linear(None, n_actions, initialW=winit_last),
chainerrl.distribution.SoftmaxDistribution,
),
L.Linear(None, 1),
)
)
# Draw the computational graph and save it in the output directory.
fake_obss = np.zeros(
sample_env.observation_space.shape, dtype=np.float32)[None]
if args.recurrent:
fake_out, _ = model(fake_obss, None)
else:
fake_out = model(fake_obss)
chainerrl.misc.draw_computational_graph(
[fake_out], os.path.join(args.outdir, 'model'))
opt = chainer.optimizers.Adam(alpha=args.lr, eps=1e-5)
opt.setup(model)
opt.add_hook(chainer.optimizer.GradientClipping(0.5))
def phi(x):
# Feature extractor
return np.asarray(x, dtype=np.float32) / 255
agent = PPO(
model,
opt,
gpu=args.gpu,
phi=phi,
update_interval=args.update_interval,
minibatch_size=args.batchsize,
epochs=args.epochs,
clip_eps=0.1,
clip_eps_vf=None,
standardize_advantages=True,
entropy_coef=1e-2,
recurrent=args.recurrent,
)
if args.load_agent:
agent.load(args.load_agent)
if (args.mode=='train'):
step_hooks = []
# Linearly decay the learning rate to zero
def lr_setter(env, agent, value):
agent.optimizer.alpha = value
step_hooks.append(
experiments.LinearInterpolationHook(
args.steps, args.lr, 0, lr_setter))
experiments.train_agent_batch_with_evaluation(
agent=agent,
env=make_batch_env(False),
eval_env=make_batch_env(True),
outdir=args.outdir,
steps=args.steps,
eval_n_steps=None,
eval_n_episodes=args.eval_n_runs,
step_offset=args.step_offset,
checkpoint_freq=args.checkpoint_frequency,
eval_interval=args.eval_interval,
log_interval=args.log_interval,
save_best_so_far_agent=False,
step_hooks=step_hooks,
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, train_env):
logging.basicConfig(level=args.logger_level)
# Set a random seed used in ChainerRL
misc.set_random_seed(args.seed, gpus=(args.gpu, ))
if not (args.demo and args.load):
args.outdir = experiments.prepare_output_dir(args, args.outdir)
temp = args.outdir.split('/')[-1]
dst = args.outdir.strip(temp)
def make_env(test):
env = gym.make(args.env)
if test:
episode_length = args.eval_episode_length
else:
episode_length = args.episode_length
env.initialize_environment(
case=args.state_rep,
n_historical_events=args.n_historical_events,
episode_length=episode_length,
n_experts=args.n_experts,
n_demos_per_expert=1,
n_expert_time_steps=args.length_expert_TS,
seed_agent=args.seed_agent,
seed_expert=args.seed_expert,
adam_days=args.adam_days)
# Use different random seeds for train and test envs
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:
env = gym.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:
env = chainerrl.wrappers.Render(env)
return env
sample_env = gym.make(args.env)
sample_env.initialize_environment(
case=args.state_rep,
n_historical_events=args.n_historical_events,
episode_length=args.episode_length,
n_experts=args.n_experts,
n_demos_per_expert=1,
n_expert_time_steps=args.length_expert_TS,
seed_agent=args.seed_agent,
seed_expert=args.seed_expert,
adam_days=args.adam_days)
demonstrations = sample_env.generate_expert_trajectories(out_dir=dst,
eval=False)
timestep_limit = None #sample_env.spec.tags.get('wrapper_config.TimeLimit.max_episode_steps') # This value is None
# Generate expert data for evaluation
temp_env = gym.make(args.env)
temp_env.initialize_environment(
case=args.state_rep,
n_historical_events=args.n_historical_events,
episode_length=
0, # This parameter does not really matter since we create this env only for generating samples
n_experts=args.n_experts,
n_demos_per_expert=1, # We do not perform any clustering right now
# n_demos_per_expert=args.n_demos_per_expert, # How large should the expert cluster be?
n_expert_time_steps=args.
eval_episode_length, # How long should each expert trajectory be?
seed_expert=args.seed_expert,
adam_days=args.adam_days)
temp_env.generate_expert_trajectories(out_dir=dst, eval=True)
obs_space = sample_env.observation_space
action_space = sample_env.action_space
# Normalize observations based on their empirical mean and variance
if args.state_rep == 1:
obs_dim = obs_space.low.size
elif args.state_rep == 2 or args.state_rep == 21 or args.state_rep == 22 or args.state_rep == 24 or args.state_rep == 4 or args.state_rep == 221 or args.state_rep == 222 \
or args.state_rep == 71 or args.state_rep == 17 or args.state_rep == 81:
obs_dim = obs_space.n
elif args.state_rep == 3 or args.state_rep == 11 or args.state_rep == 23 or args.state_rep == 31 or args.state_rep == 7:
obs_dim = obs_space.nvec.size
else:
raise NotImplementedError
if args.normalize_obs:
obs_normalizer = chainerrl.links.EmpiricalNormalization(
obs_dim,
clip_threshold=5) # shape: Shape of input values except batch axis
else:
obs_normalizer = None
# Switch policy types accordingly to action space types
if args.arch == 'FFSoftmax':
model = A3CFFSoftmax(obs_dim,
action_space.n,
hidden_sizes=args.G_layers)
elif args.arch == 'FFMellowmax':
model = A3CFFMellowmax(obs_space.low.size, action_space.n)
elif args.arch == 'FFGaussian':
model = A3CFFGaussian(obs_space.low.size,
action_space,
bound_mean=args.bound_mean)
opt = chainer.optimizers.Adam(alpha=args.lr, eps=10e-1)
opt.setup(model)
if args.show_D_dummy: # Let discriminator see dummy
input_dim_D = obs_dim + 1
elif not args.show_D_dummy: # Do not let discriminator see dummy
if args.state_rep == 21 or args.state_rep == 17:
input_dim_D = obs_dim + 1
else:
input_dim_D = obs_dim + 1 - args.n_experts
if args.weight_decay > 0:
opt.add_hook(NonbiasWeightDecay(args.weight_decay))
if args.algo == 'ppo':
agent = PPO(
model,
opt,
obs_normalizer=obs_normalizer,
gpu=args.gpu,
update_interval=args.update_interval,
minibatch_size=args.batchsize,
epochs=args.epochs,
clip_eps_vf=None,
entropy_coef=args.entropy_coef,
standardize_advantages=args.standardize_advantages,
)
elif args.algo == 'gail':
from customer_behaviour.algorithms.irl.gail import GAIL as G
from customer_behaviour.algorithms.irl.gail import Discriminator as D
demonstrations = np.load(dst + '/expert_trajectories.npz')
D = D(gpu=args.gpu,
input_dim=input_dim_D,
hidden_sizes=args.D_layers,
loss_type=args.loss_type)
agent = G(env=sample_env,
demonstrations=demonstrations,
discriminator=D,
model=model,
optimizer=opt,
obs_normalizer=obs_normalizer,
gpu=args.gpu,
update_interval=args.update_interval,
minibatch_size=args.batchsize,
epochs=args.epochs,
clip_eps_vf=None,
entropy_coef=args.entropy_coef,
standardize_advantages=args.standardize_advantages,
args=args)
elif args.algo == 'airl':
from customer_behaviour.algorithms.irl.airl import AIRL as G
from customer_behaviour.algorithms.irl.airl import Discriminator as D
# obs_normalizer = None
demonstrations = np.load(dst + '/expert_trajectories.npz')
D = D(gpu=args.gpu,
input_dim=input_dim_D - 1,
hidden_sizes=args.D_layers) # AIRL only inputs state to D
agent = G(env=sample_env,
demonstrations=demonstrations,
discriminator=D,
model=model,
optimizer=opt,
obs_normalizer=obs_normalizer,
gpu=args.gpu,
update_interval=args.update_interval,
minibatch_size=args.batchsize,
epochs=args.epochs,
clip_eps_vf=None,
entropy_coef=args.entropy_coef,
standardize_advantages=args.standardize_advantages,
noise=args.noise,
n_experts=args.n_experts,
episode_length=args.episode_length,
adam_days=args.adam_days,
dummy_D=args.show_D_dummy)
elif args.algo == 'mmct-gail':
from customer_behaviour.algorithms.irl.gail.mmct_gail import MMCTGAIL as G
from customer_behaviour.algorithms.irl.gail import Discriminator as D
demonstrations = np.load(dst + '/expert_trajectories.npz')
D = D(gpu=args.gpu,
input_dim=input_dim_D,
hidden_sizes=args.D_layers,
loss_type=args.loss_type)
agent = G(env=sample_env,
demonstrations=demonstrations,
discriminator=D,
model=model,
optimizer=opt,
obs_normalizer=obs_normalizer,
gpu=args.gpu,
update_interval=args.update_interval,
minibatch_size=args.batchsize,
epochs=args.epochs,
clip_eps_vf=None,
entropy_coef=args.entropy_coef,
standardize_advantages=args.standardize_advantages,
args=args)
if args.load:
# By default, not in here
agent.load(args.load)
if args.demo:
# By default, not in here
env = make_env(True)
eval_stats = experiments.eval_performance(
env=env,
agent=agent,
n_steps=None,
n_episodes=args.eval_n_runs,
max_episode_len=timestep_limit)
print('n_runs: {} mean: {} median: {} stdev {}'.format(
args.eval_n_runs, eval_stats['mean'], eval_stats['median'],
eval_stats['stdev']))
outdir = args.load if args.load else args.outdir
save_agent_demo(make_env(False), agent, outdir)
else:
# Linearly decay the learning rate to zero
def lr_setter(env, agent, value):
agent.optimizer.alpha = value
lr_decay_hook = experiments.LinearInterpolationHook(
args.steps, args.lr, 0, lr_setter)
# Linearly decay the clipping parameter to zero
def clip_eps_setter(env, agent, value):
agent.clip_eps = max(value, 1e-8)
clip_eps_decay_hook = experiments.LinearInterpolationHook(
args.steps, 0.2, 0, clip_eps_setter)
if train_env is None:
experiments.train_agent_with_evaluation(
agent=agent,
env=make_env(
False
), # Environment train the agent against (False -> scaled rewards)
eval_env=make_env(True), # Environment used for evaluation
outdir=args.outdir,
steps=args.
steps, # Total number of timesteps for training (args.n_training_episodes*args.episode_length)
eval_n_steps=
None, # Number of timesteps at each evaluation phase
eval_n_episodes=args.
eval_n_runs, # Number of episodes at each evaluation phase (default: 10)
eval_interval=args.
eval_interval, # Interval of evaluation (defualt: 10000 steps (?))
train_max_episode_len=
timestep_limit, # Maximum episode length during training (is None)
save_best_so_far_agent=False,
step_hooks=[
lr_decay_hook,
clip_eps_decay_hook,
],
checkpoint_freq=args.eval_interval)
else:
experiments.train_agent_batch_with_evaluation(
agent=agent,
env=train_env,
steps=args.steps,
eval_n_steps=None,
eval_n_episodes=args.eval_n_runs,
eval_interval=args.eval_interval,
outdir=args.outdir,
max_episode_len=timestep_limit,
eval_max_episode_len=None,
eval_env=make_env(True),
step_hooks=[
lr_decay_hook,
clip_eps_decay_hook,
],
save_best_so_far_agent=False,
checkpoint_freq=args.eval_interval,
log_interval=args.update_interval)
save_agent_demo(
make_env(True), agent, args.outdir, 10 * args.eval_episode_length
) # originally it was make_env(test=False) which seems strange
# Move result files to correct folder and remove empty folder
move_dir(args.outdir, dst)
os.rmdir(args.outdir)
if args.save_results:
print('Saving result...')
res2.save_data(dst, 10000, 50, N=1)
print('Running evaluate policy...')
ep.eval_policy(a_dir_path=dst)
# else:
# if args.n_experts <= 10:
# print('Running evaluate policy...')
# ep.eval_policy(a_dir_path=dst)
# # print('Running evaluate training...')
# # ets.eval_training(a_dir_path=dst)
# print('Done')
if args.save_report_material:
print('Saving dataframe...')
if args.state_rep == 21:
if args.algo == 'gail':
folder_name = 'gail'
elif args.algo == 'airl':
folder_name = 'airl'
elif args.state_rep == 22:
if args.algo == 'gail':
folder_name = 'gail_dummies'
elif args.algo == 'airl':
folder_name = 'airl_dummies'
elif args.state_rep == 81:
if args.algo == 'gail':
folder_name = 'gail_adams'
elif args.algo == 'airl':
folder_name = 'airl_adams'
elif args.state_rep == 17:
folder_name = 'ail'
elif args.state_rep == 221:
folder_name = 'ail_dummies'
elif args.state_rep == 71:
folder_name = 'ail_adams'
report_material.save_df(dst, folder_name)
if args.save_folder is not None:
print('Saving result to ' + args.save_folder)
os.makedirs(os.path.join(os.getcwd(), args.save_folder), exist_ok=True)
from distutils.dir_util import copy_tree
copy_tree(
os.path.join(os.getcwd(), dst),
os.path.join(os.getcwd(), args.save_folder,
args.outdir.split('/')[-2]))