def test_append_and_sample(self):
rbuf = replay_buffer.PrioritizedEpisodicReplayBuffer(
capacity=self.capacity,
normalize_by_max=self.normalize_by_max,
default_priority_func=self.default_priority_func,
uniform_ratio=self.uniform_ratio,
wait_priority_after_sampling=self.wait_priority_after_sampling,
return_sample_weights=self.return_sample_weights)
for n in [10, 15, 5] * 3:
transs = [
dict(state=i,
action=100 + i,
reward=200 + i,
next_state=i + 1,
next_action=101 + i,
is_state_terminal=(i == n - 1)) for i in range(n)
]
for trans in transs:
rbuf.append(**trans)
self.assertEqual(len(rbuf), 90)
self.assertEqual(rbuf.n_episodes, 9)
for k in [10, 30, 90]:
s = rbuf.sample(k)
self.assertEqual(len(s), k)
for k in [1, 3, 9]:
ret = rbuf.sample_episodes(k)
if self.return_sample_weights:
s, wt = ret
self.assertEqual(len(s), k)
self.assertEqual(len(wt), k)
else:
s = ret
self.assertEqual(len(s), k)
if self.wait_priority_after_sampling:
rbuf.update_errors([1.0] * k)
ret = rbuf.sample_episodes(k, max_len=10)
if self.return_sample_weights:
s, wt = ret
self.assertEqual(len(s), k)
self.assertEqual(len(wt), k)
else:
s = ret
if self.wait_priority_after_sampling:
rbuf.update_errors([1.0] * k)
for ep in s:
self.assertLessEqual(len(ep), 10)
for t0, t1 in zip(ep, ep[1:]):
self.assertEqual(t0['next_state'], t1['state'])
self.assertEqual(t0['next_action'], t1['action'])
def create_value_based_learner(cfg_name):
"""
Creates a learner that can be used with value based algorithms from chainerrl.
:param cfg_name: type str, the name of the config
:return: chainerrl agent specified in config
"""
vb_config = Config(cfg_name)
network = getattr(models, vb_config.get_str('BASIC', 'network'))(
**vb_config.get_section('NETWORK'))
q_func = q_functions.SingleModelStateQFunctionWithDiscreteAction(model=network)
opt = getattr(optimizers, vb_config.get_str('BASIC', 'optimizer'))(
**vb_config.get_section('OPTIMIZER'))
opt.setup(q_func)
opt.add_hook(
optimizer.GradientClipping(threshold=vb_config.get_float('BASIC', 'grad_clip')))
rep_buf = replay_buffer.PrioritizedEpisodicReplayBuffer(
capacity=vb_config.get_int('MEMORY_BUFFER', 'episodic_buffer_size'),
wait_priority_after_sampling=vb_config.get_bool('MEMORY_BUFFER',
'wait_priority_after_sampling'))
explorer = explorers.LinearDecayEpsilonGreedy(
random_action_func=lambda: np.random.random_integers(0, vb_config.get_int('NETWORK',
'output_dim') - 1),
**vb_config.get_section('EXPLORER'))
try:
learner = getattr(agents, vb_config.get_str('BASIC', 'learner'))(q_function=q_func,
optimizer=opt,
replay_buffer=rep_buf,
phi=lambda x: x,
explorer=explorer,
**vb_config.get_section(
'ALGORITHM'))
if vb_config.get_str('BASIC', 'load_path'):
learner.load(os.path.join(get_results_path(), vb_config.get_str('BASIC', 'load_path')))
except AttributeError as e:
logger.log(msg='Cannot find model {} in chainerrl.agents'.format(
vb_config.get_str('BASIC', 'learner')),
level=logging.ERROR)
raise e
logger.log(msg='Created learner {}'.format(learner.__class__.__name__),
level=logging.INFO)
logger.log(msg='Model parameters {}'.format(
' '.join([name + ':' + str(value) for name, value in
vb_config.get_section('EXPERIMENT').items()])), level=logging.INFO)
logger.log(msg='Explorer parameters {}'.format(
' '.join([name + ':' + str(value) for name, value in
vb_config.get_section('EXPLORER').items()])), level=logging.INFO)
return learner
def test(self):
if self.replay_buffer_type == 'EpisodicReplayBuffer':
rbuf = replay_buffer.EpisodicReplayBuffer(capacity=None)
elif self.replay_buffer_type == 'PrioritizedEpisodicReplayBuffer':
rbuf = replay_buffer.PrioritizedEpisodicReplayBuffer(capacity=None)
else:
assert False
# 2 transitions for env_id=0
for _ in range(2):
trans1 = dict(state=0,
action=1,
reward=2,
next_state=3,
next_action=4,
is_state_terminal=False)
rbuf.append(env_id=0, **trans1)
# 4 transitions for env_id=1 with a terminal state
for i in range(4):
trans1 = dict(state=0,
action=1,
reward=2,
next_state=3,
next_action=4,
is_state_terminal=(i == 3))
rbuf.append(env_id=1, **trans1)
# 9 transitions for env_id=2
for _ in range(9):
trans1 = dict(state=0,
action=1,
reward=2,
next_state=3,
next_action=4,
is_state_terminal=False)
rbuf.append(env_id=2, **trans1)
# It should have 4 transitions from env_id=1
self.assertEqual(len(rbuf), 4)
# env_id=0 episode ends
rbuf.stop_current_episode(env_id=0)
# Now it should have 4 + 2 = 6 transitions
self.assertEqual(len(rbuf), 6)
# env_id=2 episode ends
rbuf.stop_current_episode(env_id=2)
# Finally it should have 4 + 2 + 9 = 15 transitions
self.assertEqual(len(rbuf), 15)
def main():
import logging
logging.basicConfig(level=logging.DEBUG)
parser = argparse.ArgumentParser()
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('--env', type=str, default='CartPole-v1')
parser.add_argument('--seed', type=int, default=0)
parser.add_argument('--gpu', type=int, default=0)
parser.add_argument('--final-exploration-steps', type=int, default=1000)
parser.add_argument('--start-epsilon', type=float, default=1.0)
parser.add_argument('--end-epsilon', type=float, default=0.1)
parser.add_argument('--demo', action='store_true', default=False)
parser.add_argument('--load', type=str, default=None)
parser.add_argument('--steps', type=int, default=10**8)
parser.add_argument('--prioritized-replay', action='store_true')
parser.add_argument('--episodic-replay', action='store_true')
parser.add_argument('--replay-start-size', type=int, default=50)
parser.add_argument('--target-update-interval', type=int, default=100)
parser.add_argument('--target-update-method', type=str, default='hard')
parser.add_argument('--soft-update-tau', type=float, default=1e-2)
parser.add_argument('--update-interval', type=int, default=1)
parser.add_argument('--eval-n-runs', type=int, default=100)
parser.add_argument('--eval-interval', type=int, default=1000)
parser.add_argument('--n-hidden-channels', type=int, default=12)
parser.add_argument('--n-hidden-layers', type=int, default=3)
parser.add_argument('--gamma', type=float, default=0.95)
parser.add_argument('--minibatch-size', type=int, default=None)
parser.add_argument('--render-train', action='store_true')
parser.add_argument('--render-eval', action='store_true')
parser.add_argument('--monitor', action='store_true')
parser.add_argument('--reward-scale-factor', type=float, default=1.0)
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,
argv=sys.argv)
print('Output files are saved in {}'.format(args.outdir))
def make_env(test):
env = gym.make(args.env)
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_eval and test) or (args.render_train and not test)):
env = chainerrl.wrappers.Render(env)
return env
env = make_env(test=False)
timestep_limit = env.spec.tags.get(
'wrapper_config.TimeLimit.max_episode_steps')
obs_size = env.observation_space.low.size
action_space = env.action_space
n_atoms = 51
v_max = 500
v_min = 0
n_actions = action_space.n
q_func = q_functions.DistributionalFCStateQFunctionWithDiscreteAction(
obs_size,
n_actions,
n_atoms,
v_min,
v_max,
n_hidden_channels=args.n_hidden_channels,
n_hidden_layers=args.n_hidden_layers)
# Use epsilon-greedy for exploration
explorer = explorers.LinearDecayEpsilonGreedy(args.start_epsilon,
args.end_epsilon,
args.final_exploration_steps,
action_space.sample)
opt = optimizers.Adam(1e-3)
opt.setup(q_func)
rbuf_capacity = 50000 # 5 * 10 ** 5
if args.episodic_replay:
if args.minibatch_size is None:
args.minibatch_size = 4
if args.prioritized_replay:
betasteps = (args.steps - args.replay_start_size) \
// args.update_interval
rbuf = replay_buffer.PrioritizedEpisodicReplayBuffer(
rbuf_capacity, betasteps=betasteps)
else:
rbuf = replay_buffer.EpisodicReplayBuffer(rbuf_capacity)
else:
if args.minibatch_size is None:
args.minibatch_size = 32
if args.prioritized_replay:
betasteps = (args.steps - args.replay_start_size) \
// args.update_interval
rbuf = replay_buffer.PrioritizedReplayBuffer(rbuf_capacity,
betasteps=betasteps)
else:
rbuf = replay_buffer.ReplayBuffer(rbuf_capacity)
agent = chainerrl.agents.CategoricalDQN(
q_func,
opt,
rbuf,
gpu=args.gpu,
gamma=args.gamma,
explorer=explorer,
replay_start_size=args.replay_start_size,
target_update_interval=args.target_update_interval,
update_interval=args.update_interval,
minibatch_size=args.minibatch_size,
target_update_method=args.target_update_method,
soft_update_tau=args.soft_update_tau,
episodic_update=args.episodic_replay,
episodic_update_len=16)
if args.load:
agent.load(args.load)
eval_env = make_env(test=True)
if args.demo:
eval_stats = experiments.eval_performance(
env=eval_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:
experiments.train_agent_with_evaluation(
agent=agent,
env=env,
steps=args.steps,
eval_n_runs=args.eval_n_runs,
eval_interval=args.eval_interval,
outdir=args.outdir,
eval_env=eval_env,
max_episode_len=timestep_limit)
def main():
import logging
logging.basicConfig(level=logging.DEBUG)
parser = argparse.ArgumentParser()
parser.add_argument('--outdir', type=str, default='dqn_out')
parser.add_argument('--env', type=str, default='Pendulum-v0')
parser.add_argument('--seed', type=int, default=None)
parser.add_argument('--gpu', type=int, default=0)
parser.add_argument('--final-exploration-steps', type=int, default=10**4)
parser.add_argument('--start-epsilon', type=float, default=1.0)
parser.add_argument('--end-epsilon', type=float, default=0.1)
parser.add_argument('--demo', action='store_true', default=False)
parser.add_argument('--load', type=str, default=None)
parser.add_argument('--steps', type=int, default=10**5)
parser.add_argument('--prioritized-replay', action='store_true')
parser.add_argument('--episodic-replay', action='store_true')
parser.add_argument('--replay-start-size', type=int, default=1000)
parser.add_argument('--target-update-interval', type=int, default=10**2)
parser.add_argument('--target-update-method', type=str, default='hard')
parser.add_argument('--soft-update-tau', type=float, default=1e-2)
parser.add_argument('--update-interval', type=int, default=1)
parser.add_argument('--eval-n-runs', type=int, default=100)
parser.add_argument('--eval-interval', type=int, default=10**4)
parser.add_argument('--n-hidden-channels', type=int, default=100)
parser.add_argument('--n-hidden-layers', type=int, default=2)
parser.add_argument('--gamma', type=float, default=0.99)
parser.add_argument('--minibatch-size', type=int, default=None)
parser.add_argument('--render-train', action='store_true')
parser.add_argument('--render-eval', action='store_true')
parser.add_argument('--monitor', action='store_true')
parser.add_argument('--reward-scale-factor', type=float, default=1e-3)
args = parser.parse_args()
args.outdir = experiments.prepare_output_dir(args,
args.outdir,
argv=sys.argv)
print('Output files are saved in {}'.format(args.outdir))
if args.seed is not None:
misc.set_random_seed(args.seed)
def clip_action_filter(a):
return np.clip(a, action_space.low, action_space.high)
def make_env(for_eval):
env = gym.make(args.env)
if args.monitor:
env = gym.wrappers.Monitor(env, args.outdir)
if isinstance(env.action_space, spaces.Box):
misc.env_modifiers.make_action_filtered(env, clip_action_filter)
if not for_eval:
misc.env_modifiers.make_reward_filtered(
env, lambda x: x * args.reward_scale_factor)
if ((args.render_eval and for_eval)
or (args.render_train and not for_eval)):
misc.env_modifiers.make_rendered(env)
return env
env = make_env(for_eval=False)
timestep_limit = env.spec.tags.get(
'wrapper_config.TimeLimit.max_episode_steps')
obs_space = env.observation_space
obs_size = obs_space.low.size
action_space = env.action_space
if isinstance(action_space, spaces.Box):
action_size = action_space.low.size
# Use NAF to apply DQN to continuous action spaces
q_func = q_functions.FCQuadraticStateQFunction(
obs_size,
action_size,
n_hidden_channels=args.n_hidden_channels,
n_hidden_layers=args.n_hidden_layers,
action_space=action_space)
# Use the Ornstein-Uhlenbeck process for exploration
ou_sigma = (action_space.high - action_space.low) * 0.2
explorer = explorers.AdditiveOU(sigma=ou_sigma)
else:
n_actions = action_space.n
q_func = q_functions.FCStateQFunctionWithDiscreteAction(
obs_size,
n_actions,
n_hidden_channels=args.n_hidden_channels,
n_hidden_layers=args.n_hidden_layers)
# Use epsilon-greedy for exploration
explorer = explorers.LinearDecayEpsilonGreedy(
args.start_epsilon, args.end_epsilon, args.final_exploration_steps,
action_space.sample)
# Draw the computational graph and save it in the output directory.
chainerrl.misc.draw_computational_graph(
[q_func(np.zeros_like(obs_space.low, dtype=np.float32)[None])],
os.path.join(args.outdir, 'model'))
opt = optimizers.Adam()
opt.setup(q_func)
rbuf_capacity = 5 * 10**5
if args.episodic_replay:
if args.minibatch_size is None:
args.minibatch_size = 4
if args.prioritized_replay:
betasteps = (args.steps - args.replay_start_size) \
// args.update_interval
rbuf = replay_buffer.PrioritizedEpisodicReplayBuffer(
rbuf_capacity, betasteps=betasteps)
else:
rbuf = replay_buffer.EpisodicReplayBuffer(rbuf_capacity)
else:
if args.minibatch_size is None:
args.minibatch_size = 32
if args.prioritized_replay:
betasteps = (args.steps - args.replay_start_size) \
// args.update_interval
rbuf = replay_buffer.PrioritizedReplayBuffer(rbuf_capacity,
betasteps=betasteps)
else:
rbuf = replay_buffer.ReplayBuffer(rbuf_capacity)
def phi(obs):
return obs.astype(np.float32)
agent = DQN(q_func,
opt,
rbuf,
gpu=args.gpu,
gamma=args.gamma,
explorer=explorer,
replay_start_size=args.replay_start_size,
target_update_interval=args.target_update_interval,
update_interval=args.update_interval,
phi=phi,
minibatch_size=args.minibatch_size,
target_update_method=args.target_update_method,
soft_update_tau=args.soft_update_tau,
episodic_update=args.episodic_replay,
episodic_update_len=16)
if args.load:
agent.load(args.load)
eval_env = make_env(for_eval=True)
if args.demo:
eval_stats = experiments.eval_performance(
env=eval_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:
experiments.train_agent_with_evaluation(
agent=agent,
env=env,
steps=args.steps,
eval_n_runs=args.eval_n_runs,
eval_interval=args.eval_interval,
outdir=args.outdir,
eval_env=eval_env,
max_episode_len=timestep_limit)
def main():
parser = argparse.ArgumentParser()
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('--seed', type=int, default=123,
help='Random seed [0, 2 ** 32)')
parser.add_argument('--gpu', type=int, default=-1)
parser.add_argument('--final-exploration-steps',
type=int, default=10 ** 4)
parser.add_argument('--start-epsilon', type=float, default=1.0)
parser.add_argument('--end-epsilon', type=float, default=0.1)
parser.add_argument('--noisy-net-sigma', type=float, default=None)
parser.add_argument('--demo', action='store_true', default=False)
parser.add_argument('--load', type=str, default=None)
parser.add_argument('--steps', type=int, default=50000)
parser.add_argument('--prioritized-replay', action='store_true', default=False)
parser.add_argument('--episodic-replay', action='store_true', default=False)
parser.add_argument('--replay-start-size', type=int, default=1000)
parser.add_argument('--target-update-interval', type=int, default=10 ** 2)
parser.add_argument('--target-update-method', type=str, default='hard')
parser.add_argument('--soft-update-tau', type=float, default=1e-2)
parser.add_argument('--update-interval', type=int, default=1)
parser.add_argument('--eval-n-runs', type=int, default=50)
parser.add_argument('--eval-interval', type=int, default=10 ** 3)
parser.add_argument('--n-hidden-channels', type=int, default=512)
parser.add_argument('--n-hidden-layers', type=int, default=2)
parser.add_argument('--gamma', type=float, default=0.99)
parser.add_argument('--minibatch-size', type=int, default=None)
parser.add_argument('--render-train', action='store_true')
parser.add_argument('--render-eval', action='store_true')
parser.add_argument('--monitor', action='store_true', default=True)
parser.add_argument('--reward-scale-factor', type=float, default=1e-3)
args = parser.parse_args()
# Set a random seed used in ChainerRL
misc.set_random_seed(args.seed)
args.outdir = experiments.prepare_output_dir(
args, args.outdir, argv=sys.argv)
print('Output files are saved in {}'.format(args.outdir))
def make_env(test):
ENV_NAME = 'malware-test-v0' if test else 'malware-v0'
env = gym.make(ENV_NAME)
# 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)
if args.monitor:
env = gym.wrappers.Monitor(env, args.outdir)
# if not test:
# misc.env_modifiers.make_reward_filtered(
# env, lambda x: x * args.reward_scale_factor)
if ((args.render_eval and test) or
(args.render_train and not test)):
misc.env_modifiers.make_rendered(env)
return env
env = make_env(test=False)
timestep_limit = 80
obs_space = env.observation_space
obs_size = obs_space.shape[0]
action_space = env.action_space
n_actions = action_space.n
q_func = q_functions.FCStateQFunctionWithDiscreteAction(
obs_size, n_actions,
n_hidden_channels=args.n_hidden_channels,
n_hidden_layers=args.n_hidden_layers)
if args.gpu >= 0:
q_func.to_gpu(args.gpu)
# Use epsilon-greedy for exploration
explorer = explorers.LinearDecayEpsilonGreedy(
args.start_epsilon, args.end_epsilon, args.final_exploration_steps,
action_space.sample)
if args.noisy_net_sigma is not None:
links.to_factorized_noisy(q_func)
# Turn off explorer
explorer = explorers.Greedy()
# Draw the computational graph and save it in the output directory.
if args.gpu < 0:
chainerrl.misc.draw_computational_graph(
[q_func(np.zeros_like(obs_space, dtype=np.float32)[None])],
os.path.join(args.outdir, 'model'))
opt = optimizers.Adam()
opt.setup(q_func)
rbuf_capacity = 5 * 10 ** 5
if args.episodic_replay:
if args.minibatch_size is None:
args.minibatch_size = 4
if args.prioritized_replay:
betasteps = (args.steps - args.replay_start_size) \
// args.update_interval
rbuf = replay_buffer.PrioritizedEpisodicReplayBuffer(
rbuf_capacity, betasteps=betasteps)
else:
rbuf = replay_buffer.EpisodicReplayBuffer(rbuf_capacity)
else:
if args.minibatch_size is None:
args.minibatch_size = 32
if args.prioritized_replay:
betasteps = (args.steps - args.replay_start_size) \
// args.update_interval
rbuf = replay_buffer.PrioritizedReplayBuffer(
rbuf_capacity, betasteps=betasteps)
else:
rbuf = replay_buffer.ReplayBuffer(rbuf_capacity)
def phi(obs):
return obs.astype(np.float32)
agent = DoubleDQN(q_func, opt, rbuf, gamma=args.gamma,
explorer=explorer, replay_start_size=args.replay_start_size,
target_update_interval=args.target_update_interval,
update_interval=args.update_interval,
phi=phi, minibatch_size=args.minibatch_size,
target_update_method=args.target_update_method,
soft_update_tau=args.soft_update_tau,
episodic_update=args.episodic_replay, episodic_update_len=16)
if args.load:
agent.load(args.load)
eval_env = make_env(test=True)
if args.demo:
eval_stats = experiments.eval_performance(
env=eval_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:
q_hook = PlotHook('Average Q Value')
loss_hook = PlotHook('Average Loss', plot_index=1)
experiments.train_agent_with_evaluation(
agent=agent, env=env, steps=args.steps,
eval_n_runs=args.eval_n_runs, eval_interval=args.eval_interval,
outdir=args.outdir, eval_env=eval_env,
max_episode_len=timestep_limit,
step_hooks=[q_hook, loss_hook],
successful_score=7
)
def create_ddqn_agent(env, args):
obs_size = env.observation_space.shape[0]
action_space = env.action_space
n_actions = action_space.n
# q_func = q_functions.FCStateQFunctionWithDiscreteAction(
# obs_size, n_actions,
# n_hidden_channels=args.n_hidden_channels,
# n_hidden_layers=args.n_hidden_layers)
q_func = QFunction(obs_size, n_actions)
if args.gpu:
q_func.to_gpu(args.gpu)
# Draw the computational graph and save it in the output directory.
if not args.test and not args.gpu:
chainerrl.misc.draw_computational_graph(
[q_func(np.zeros_like(env.observation_space, dtype=np.float32)[None])],
os.path.join(args.outdir, 'model'))
# Use epsilon-greedy for exploration
explorer = explorers.LinearDecayEpsilonGreedy(
args.start_epsilon, args.end_epsilon, args.final_exploration_steps,
action_space.sample)
# explorer = explorers.Boltzmann()
# explorer = explorers.ConstantEpsilonGreedy(
# epsilon=0.3, random_action_func=env.action_space.sample)
opt = optimizers.Adam()
opt.setup(q_func)
rbuf_capacity = 5 * 10 ** 3
if args.episodic_replay:
if args.minibatch_size is None:
args.minibatch_size = 4
if args.prioritized_replay:
betasteps = (args.steps - args.replay_start_size) // args.update_interval
rbuf = replay_buffer.PrioritizedEpisodicReplayBuffer(rbuf_capacity, betasteps=betasteps)
else:
rbuf = replay_buffer.EpisodicReplayBuffer(rbuf_capacity)
else:
if args.minibatch_size is None:
args.minibatch_size = 32
if args.prioritized_replay:
betasteps = (args.steps - args.replay_start_size) // args.update_interval
rbuf = replay_buffer.PrioritizedReplayBuffer(rbuf_capacity, betasteps=betasteps)
else:
rbuf = replay_buffer.ReplayBuffer(rbuf_capacity)
# Chainer only accepts numpy.float32 by default, make sure
# a converter as a feature extractor function phi.
phi = lambda x: x.astype(np.float32, copy=False)
agent = chainerrl.agents.DoubleDQN(q_func, opt, rbuf, gamma=args.gamma,
explorer=explorer, replay_start_size=args.replay_start_size,
target_update_interval=args.target_update_interval,
update_interval=args.update_interval,
phi=phi, minibatch_size=args.minibatch_size,
target_update_method=args.target_update_method,
soft_update_tau=args.soft_update_tau,
episodic_update=args.episodic_replay,
episodic_update_len=16)
return agent
def main():
import logging
logging.basicConfig(level=logging.WARNING)
args = parser.parse_args()
args.outdir = experiments.prepare_output_dir(args,
args.outdir,
argv=sys.argv)
print('Output files are saved in {}'.format(args.outdir))
if args.seed is not None:
misc.set_random_seed(args.seed)
option2id, all_guesses = load_quizbowl()
train_iter = QuestionIterator(all_guesses[c.BUZZER_DEV_FOLD],
option2id,
batch_size=1,
make_vector=dense_vector)
env = BuzzingGame(train_iter)
timestep_limit = 300
obs_size = env.observation_size
action_space = env.action_space
n_actions = action_space.n
q_func = q_functions.FCStateQFunctionWithDiscreteAction(
obs_size,
n_actions,
n_hidden_channels=args.n_hidden_channels,
n_hidden_layers=args.n_hidden_layers)
# Use epsilon-greedy for exploration
explorer = explorers.LinearDecayEpsilonGreedy(args.start_epsilon,
args.end_epsilon,
args.final_exploration_steps,
action_space.sample)
opt = optimizers.Adam()
opt.setup(q_func)
rbuf_capacity = 5 * 10**5
if args.episodic_replay:
if args.minibatch_size is None:
args.minibatch_size = 4
if args.replay_start_size is None:
args.replay_start_size = 10
if args.prioritized_replay:
betasteps = \
(args.steps - timestep_limit * args.replay_start_size) \
// args.update_interval
rbuf = replay_buffer.PrioritizedEpisodicReplayBuffer(
rbuf_capacity, betasteps=betasteps)
else:
rbuf = replay_buffer.EpisodicReplayBuffer(rbuf_capacity)
else:
if args.minibatch_size is None:
args.minibatch_size = 32
if args.replay_start_size is None:
args.replay_start_size = 1000
if args.prioritized_replay:
betasteps = (args.steps - args.replay_start_size) \
// args.update_interval
rbuf = replay_buffer.PrioritizedReplayBuffer(rbuf_capacity,
betasteps=betasteps)
else:
rbuf = replay_buffer.ReplayBuffer(rbuf_capacity)
def phi(obs):
return obs.astype(np.float32)
agent = DQN(q_func,
opt,
rbuf,
gpu=args.gpu,
gamma=args.gamma,
explorer=explorer,
replay_start_size=args.replay_start_size,
target_update_interval=args.target_update_interval,
update_interval=args.update_interval,
phi=phi,
minibatch_size=args.minibatch_size,
target_update_method=args.target_update_method,
soft_update_tau=args.soft_update_tau,
episodic_update=args.episodic_replay,
episodic_update_len=16)
if args.load:
agent.load(args.load)
eval_env = BuzzingGame(train_iter)
if args.demo:
eval_stats = experiments.eval_performance(
env=eval_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:
experiments.train_agent_with_evaluation(
agent=agent,
env=env,
steps=args.steps,
eval_n_runs=args.eval_n_runs,
eval_interval=args.eval_interval,
outdir=args.outdir,
eval_env=eval_env,
max_episode_len=timestep_limit)
serializers.save_npz('dqn.npz', q_func)
dev_iter = QuestionIterator(all_guesses[c.BUZZER_DEV_FOLD],
option2id,
batch_size=128,
make_vector=dense_vector)
dev_buzzes = get_buzzes(q_func, dev_iter)
dev_buzzes_dir = 'output/buzzer/rl/dev_buzzes.pkl'
with open(dev_buzzes_dir, 'wb') as f:
pickle.dump(dev_buzzes, f)
print('Dev buzz {} saved to {}'.format(len(dev_buzzes), dev_buzzes_dir))
report(dev_buzzes_dir)
def main():
import logging
logging.basicConfig(level=logging.DEBUG)
parser = argparse.ArgumentParser()
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('--env',
type=str,
default='MsPacman-ramNoFrameskip-v4')
parser.add_argument('--seed',
type=int,
default=0,
help='Random seed [0, 2 ** 32)')
parser.add_argument('--gpu', type=int, default=-1)
parser.add_argument('--final-exploration-steps', type=int, default=10**4)
parser.add_argument('--start-epsilon', type=float, default=1.0)
parser.add_argument('--end-epsilon', type=float, default=0.1)
parser.add_argument('--noisy-net-sigma', type=float, default=None)
parser.add_argument('--demo', action='store_true', default=False)
parser.add_argument('--load', type=str, default=None)
parser.add_argument('--steps', type=int, default=5 * 10**6)
parser.add_argument('--prioritized-replay', action='store_true')
parser.add_argument('--episodic-replay', action='store_true')
parser.add_argument('--replay-start-size', type=int, default=1000)
parser.add_argument('--target-update-interval', type=int, default=10**2)
parser.add_argument('--target-update-method', type=str, default='hard')
parser.add_argument('--soft-update-tau', type=float, default=1e-2)
parser.add_argument('--update-interval', type=int, default=1)
parser.add_argument('--eval-n-runs', type=int, default=10)
parser.add_argument('--eval-interval', type=int, default=5 * 10**4)
parser.add_argument('--n-hidden-channels', type=int, default=100)
parser.add_argument('--n-hidden-layers', type=int, default=2)
parser.add_argument('--gamma', type=float, default=0.99)
parser.add_argument('--minibatch-size', type=int, default=None)
parser.add_argument('--render-train', action='store_true')
parser.add_argument('--render-eval', action='store_true')
parser.add_argument('--monitor', action='store_true')
parser.add_argument('--reward-scale-factor', type=float, default=1e-2)
args = parser.parse_args()
args.prioritized_replay = True
# 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, argv=sys.argv)
print('Output files are saved in {}'.format(args.outdir))
def clip_action_filter(a):
return np.clip(a, action_space.low, action_space.high)
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 isinstance(env.action_space, spaces.Box):
misc.env_modifiers.make_action_filtered(env, clip_action_filter)
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_eval and test) or (args.render_train and not test)):
env = chainerrl.wrappers.Render(env)
return env
env = make_env(test=False)
timestep_limit = env.spec.tags.get(
'wrapper_config.TimeLimit.max_episode_steps')
obs_space = env.observation_space
obs_size = obs_space.low.size
action_space = env.action_space
if isinstance(action_space, spaces.Box):
action_size = action_space.low.size
# Use NAF to apply DQN to continuous action spaces
q_func = q_functions.FCQuadraticStateQFunction(
obs_size,
action_size,
n_hidden_channels=args.n_hidden_channels,
n_hidden_layers=args.n_hidden_layers,
action_space=action_space)
# Use the Ornstein-Uhlenbeck process for exploration
ou_sigma = (action_space.high - action_space.low) * 0.2
explorer = explorers.AdditiveOU(sigma=ou_sigma)
else:
n_actions = action_space.n
q_func = q_functions.FCStateQFunctionWithDiscreteAction(
obs_size,
n_actions,
n_hidden_channels=args.n_hidden_channels,
n_hidden_layers=args.n_hidden_layers)
# Use epsilon-greedy for exploration
explorer = explorers.LinearDecayEpsilonGreedy(
args.start_epsilon, args.end_epsilon, args.final_exploration_steps,
action_space.sample)
if args.noisy_net_sigma is not None:
links.to_factorized_noisy(q_func)
# Turn off explorer
explorer = explorers.Greedy()
# Draw the computational graph and save it in the output directory.
chainerrl.misc.draw_computational_graph(
[q_func(np.zeros_like(obs_space.low, dtype=np.float32)[None])],
os.path.join(args.outdir, 'model'))
opt = optimizers.Adam(eps=1e-3)
opt.setup(q_func)
rbuf_capacity = 5 * 10**5
if args.episodic_replay:
if args.minibatch_size is None:
args.minibatch_size = 4
if args.prioritized_replay:
betasteps = (args.steps - args.replay_start_size) \
// args.update_interval
rbuf = replay_buffer.PrioritizedEpisodicReplayBuffer(
rbuf_capacity, betasteps=betasteps)
else:
rbuf = replay_buffer.EpisodicReplayBuffer(rbuf_capacity)
else:
if args.minibatch_size is None:
args.minibatch_size = 32
if args.prioritized_replay:
betasteps = (args.steps - args.replay_start_size) \
// args.update_interval
rbuf = replay_buffer.PrioritizedReplayBuffer(rbuf_capacity,
betasteps=betasteps)
else:
rbuf = replay_buffer.ReplayBuffer(rbuf_capacity)
agent = chainerrl.agents.DoubleDQN(
q_func,
opt,
rbuf,
gpu=args.gpu,
gamma=args.gamma,
explorer=explorer,
replay_start_size=args.replay_start_size,
target_update_interval=args.target_update_interval,
update_interval=args.update_interval,
minibatch_size=args.minibatch_size,
target_update_method=args.target_update_method,
soft_update_tau=args.soft_update_tau,
episodic_update=args.episodic_replay,
episodic_update_len=16)
if args.load:
agent.load(args.load)
eval_env = make_env(test=True)
if args.demo:
eval_stats = experiments.eval_performance(
env=eval_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.next_agent_num = 1
def hook(env, agent, step):
if step > env.next_agent_num * 5 * 10**5:
agent.save('results/agent_' + str(env.next_agent_num))
env.next_agent_num += 1
experiments.train_agent_with_evaluation(
agent=agent,
env=env,
steps=args.steps,
eval_n_steps=None,
eval_n_episodes=args.eval_n_runs,
eval_interval=args.eval_interval,
outdir=args.outdir,
eval_env=eval_env,
train_max_episode_len=timestep_limit,
step_hooks=(hook, ))
