def set_explorer(env):
# Possible parameters:
# Initial (and max) value of epsilon at the start of the experimentation.
start_epsilon = 1.0
# Minimum value of epsilon
end_epsilon = 0.1
# Constant epsilon
cons_epsilon = 0.001
# how many steps it takes for epsilon to decay
final_exploration_steps = 10**5
# Options for exploration (more explorers at site-packages/chainerrl/explorers/)
# Option 1: Constant
constant_epsilon_explorer = explorers.ConstantEpsilonGreedy(
epsilon=cons_epsilon, random_action_func=env.action_space.sample)
# Option 2: Linear decay
decay_epsilon_explorer = explorers.LinearDecayEpsilonGreedy(
start_epsilon,
end_epsilon,
final_exploration_steps,
random_action_func=str(env.action_space.sample))
return constant_epsilon_explorer
def create_agent(self, env):
model = create_state_q_function_for_env(env)
rbuf = replay_buffer.ReplayBuffer(10**5)
opt = optimizers.Adam()
opt.setup(model)
explorer = explorers.ConstantEpsilonGreedy(
0.2, random_action_func=lambda: env.action_space.sample())
return agents.DQN(model, opt, rbuf, gamma=0.99, explorer=explorer)
def make_chainer_dqn(obs_size, action_space):
q_func = q_functions.FCStateQFunctionWithDiscreteAction(
obs_size, action_space.n, 50, 1)
explorer = explorers.ConstantEpsilonGreedy(0.1, action_space.sample)
opt = optimizers.Adam(eps=1e-2)
opt.setup(q_func)
rbuf = replay_buffer.ReplayBuffer(10**5)
agent = DQN(q_func, opt, rbuf, explorer=explorer, gamma=0.9)
return agent
def create_agent(self, env):
model = create_state_q_function_for_env(env)
opt = optimizers.Adam()
opt.setup(model)
explorer = explorers.ConstantEpsilonGreedy(
0.2, random_action_func=lambda: env.action_space.sample())
return agents.NSQ(q_function=model,
optimizer=opt,
t_max=1,
gamma=0.99,
i_target=100,
explorer=explorer)
n_actions = action_space.n #q_func = q_functions.FCStateQFunctionWithDiscreteAction( #obs_size, n_actions, #n_hidden_channels=n_hidden_channels, #n_hidden_layers=n_hidden_layers #) q_func = QFunction( obs_size, n_actions, ) # Use epsilon-greedy for exploration # Constant explorer = explorers.ConstantEpsilonGreedy( epsilon = 0.3, random_action_func=env.action_space.sample ) # Linear decay #explorer = explorers.LinearDecayEpsilonGreedy( #start_epsilon, #end_epsilon, #final_exploration_steps, #random_action_func=str(env.action_space.sample) #) # Set up Adam optimizer opt = optimizers.Adam() opt.setup(q_func) # DQN uses Experience Replay.
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--env', type=str, default='BreakoutNoFrameskip-v4')
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=0,
help='Random seed [0, 2 ** 31)')
parser.add_argument('--gpu', type=int, default=0)
parser.add_argument('--demo', action='store_true', default=False)
parser.add_argument('--load', type=str, default=None)
parser.add_argument('--use-sdl', action='store_true', default=False)
parser.add_argument('--final-exploration-frames', type=int, default=10**6)
parser.add_argument('--final-epsilon', type=float, default=0.1)
parser.add_argument('--eval-epsilon', type=float, default=0.05)
parser.add_argument('--arch',
type=str,
default='nature',
choices=['nature', 'nips', 'dueling'])
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('--replay-start-size', type=int, default=5 * 10**4)
parser.add_argument('--target-update-interval', type=int, default=10**4)
parser.add_argument('--eval-interval', type=int, default=10**5)
parser.add_argument('--update-interval', type=int, default=4)
parser.add_argument('--activation', type=str, default='relu')
parser.add_argument('--eval-n-runs', type=int, default=10)
parser.add_argument('--no-clip-delta',
dest='clip_delta',
action='store_false')
parser.set_defaults(clip_delta=True)
parser.add_argument('--agent',
type=str,
default='DQN',
choices=['DQN', 'DoubleDQN', 'PAL'])
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.')
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:
misc.env_modifiers.make_rendered(env)
return env
env = make_env(test=False)
eval_env = make_env(test=True)
n_actions = env.action_space.n
activation = parse_activation(args.activation)
q_func = parse_arch(args.arch, n_actions, activation)
# Draw the computational graph and save it in the output directory.
chainerrl.misc.draw_computational_graph(
[q_func(np.zeros((4, 84, 84), dtype=np.float32)[None])],
os.path.join(args.outdir, 'model'))
# Use the same hyper parameters as the Nature paper's
opt = optimizers.RMSpropGraves(lr=2.5e-4,
alpha=0.95,
momentum=0.0,
eps=1e-2)
opt.setup(q_func)
rbuf = replay_buffer.ReplayBuffer(10**6)
explorer = explorers.LinearDecayEpsilonGreedy(
1.0, args.final_epsilon, args.final_exploration_frames,
lambda: np.random.randint(n_actions))
def phi(x):
# Feature extractor
return np.asarray(x, dtype=np.float32) / 255
Agent = parse_agent(args.agent)
agent = Agent(q_func,
opt,
rbuf,
gpu=args.gpu,
gamma=0.99,
explorer=explorer,
replay_start_size=args.replay_start_size,
target_update_interval=args.target_update_interval,
clip_delta=args.clip_delta,
update_interval=args.update_interval,
batch_accumulator='sum',
phi=phi)
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:
# In testing DQN, randomly select 5% of actions
eval_explorer = explorers.ConstantEpsilonGreedy(
args.eval_epsilon, lambda: np.random.randint(n_actions))
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_explorer=eval_explorer,
save_best_so_far_agent=False,
max_episode_len=args.max_episode_len,
eval_env=eval_env,
)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('rom', type=str)
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=0,
help='Random seed [0, 2 ** 31)')
parser.add_argument('--gpu', type=int, default=0)
parser.add_argument('--demo', action='store_true', default=False)
parser.add_argument('--load', type=str, default=None)
parser.add_argument('--use-sdl', action='store_true', default=False)
parser.add_argument('--final-exploration-frames', type=int, default=10**6)
parser.add_argument('--final-epsilon', type=float, default=0.1)
parser.add_argument('--eval-epsilon', type=float, default=0.05)
parser.add_argument('--steps', type=int, default=10**7)
parser.add_argument('--replay-start-size', type=int, default=5 * 10**4)
parser.add_argument('--target-update-interval', type=int, default=10**4)
parser.add_argument('--eval-interval', type=int, default=10**5)
parser.add_argument('--update-interval', type=int, default=4)
parser.add_argument('--eval-n-runs', type=int, default=10)
parser.add_argument('--batch-size', type=int, default=32)
parser.add_argument('--logging-level',
type=int,
default=20,
help='Logging level. 10:DEBUG, 20:INFO etc.')
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))
# In training, life loss is considered as terminal states
env = ale.ALE(args.rom, use_sdl=args.use_sdl, seed=train_seed)
misc.env_modifiers.make_reward_clipped(env, -1, 1)
# In testing, an episode is terminated when all lives are lost
eval_env = ale.ALE(args.rom,
use_sdl=args.use_sdl,
treat_life_lost_as_terminal=False,
seed=test_seed)
n_actions = env.number_of_actions
n_atoms = 51
v_max = 10
v_min = -10
q_func = chainerrl.links.Sequence(
chainerrl.links.NatureDQNHead(),
chainerrl.q_functions.DistributionalFCStateQFunctionWithDiscreteAction(
None,
n_actions,
n_atoms,
v_min,
v_max,
n_hidden_channels=0,
n_hidden_layers=0),
)
# Draw the computational graph and save it in the output directory.
chainerrl.misc.draw_computational_graph(
[q_func(np.zeros((4, 84, 84), dtype=np.float32)[None])],
os.path.join(args.outdir, 'model'))
# Use the same hyper parameters as https://arxiv.org/abs/1707.06887
opt = chainer.optimizers.Adam(2.5e-4, eps=1e-2 / args.batch_size)
opt.setup(q_func)
rbuf = replay_buffer.ReplayBuffer(10**6)
explorer = explorers.LinearDecayEpsilonGreedy(
1.0, args.final_epsilon, args.final_exploration_frames,
lambda: np.random.randint(n_actions))
agent = chainerrl.agents.CategoricalDQN(
q_func,
opt,
rbuf,
gpu=args.gpu,
gamma=0.99,
explorer=explorer,
replay_start_size=args.replay_start_size,
target_update_interval=args.target_update_interval,
update_interval=args.update_interval,
batch_accumulator='mean',
phi=dqn_phi,
)
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:
# In testing DQN, randomly select 5% of actions
eval_explorer = explorers.ConstantEpsilonGreedy(
args.eval_epsilon, lambda: np.random.randint(n_actions))
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_explorer=eval_explorer,
save_best_so_far_agent=False,
eval_env=eval_env)
def main():
import logging
logging.basicConfig(level=logging.DEBUG)
parser = argparse.ArgumentParser()
parser.add_argument('processes', type=int)
parser.add_argument('rom', type=str)
parser.add_argument('--seed',
type=int,
default=0,
help='Random seed [0, 2 ** 31)')
parser.add_argument('--lr', type=float, default=7e-4)
parser.add_argument('--steps', type=int, default=8 * 10**7)
parser.add_argument('--use-sdl', action='store_true', default=False)
parser.add_argument('--final-exploration-frames',
type=int,
default=4 * 10**6)
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('--profile', action='store_true')
parser.add_argument('--eval-interval', type=int, default=10**6)
parser.add_argument('--eval-n-runs', type=int, default=10)
parser.add_argument('--demo', action='store_true', default=False)
parser.add_argument('--load', type=str, default=None)
args = parser.parse_args()
# Set a random seed used in ChainerRL.
# If you use more than one processes, the results will be no longer
# deterministic even with the same random seed.
misc.set_random_seed(args.seed)
# Set different random seeds for different subprocesses.
# If seed=0 and processes=4, subprocess seeds are [0, 1, 2, 3].
# If seed=1 and processes=4, subprocess seeds are [4, 5, 6, 7].
process_seeds = np.arange(args.processes) + args.seed * args.processes
assert process_seeds.max() < 2**31
args.outdir = experiments.prepare_output_dir(args, args.outdir)
print('Output files are saved in {}'.format(args.outdir))
def make_env(process_idx, test):
# Use different random seeds for train and test envs
process_seed = process_seeds[process_idx]
env_seed = 2**31 - 1 - process_seed if test else process_seed
env = 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
sample_env = make_env(0, test=False)
action_space = sample_env.action_space
assert isinstance(action_space, spaces.Discrete)
# Define a model and its optimizer
q_func = links.Sequence(links.NIPSDQNHead(), L.Linear(256, action_space.n),
DiscreteActionValue)
opt = rmsprop_async.RMSpropAsync(lr=args.lr, eps=1e-1, alpha=0.99)
opt.setup(q_func)
# Make process-specific agents to diversify exploration
def make_agent(process_idx):
# Random epsilon assignment described in the original paper
rand = random.random()
if rand < 0.4:
epsilon_target = 0.1
elif rand < 0.7:
epsilon_target = 0.01
else:
epsilon_target = 0.5
explorer = explorers.LinearDecayEpsilonGreedy(
1, epsilon_target, args.final_exploration_frames,
action_space.sample)
# Suppress the explorer logger
explorer.logger.setLevel(logging.INFO)
return nsq.NSQ(q_func,
opt,
t_max=5,
gamma=0.99,
i_target=40000,
explorer=explorer,
phi=dqn_phi)
if args.demo:
env = make_env(0, True)
agent = make_agent(0)
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:
explorer = explorers.ConstantEpsilonGreedy(0.05, action_space.sample)
# Linearly decay the learning rate to zero
def lr_setter(env, agent, value):
agent.optimizer.lr = value
lr_decay_hook = experiments.LinearInterpolationHook(
args.steps, args.lr, 0, lr_setter)
experiments.train_agent_async(outdir=args.outdir,
processes=args.processes,
make_env=make_env,
make_agent=make_agent,
profile=args.profile,
steps=args.steps,
eval_n_runs=args.eval_n_runs,
eval_interval=args.eval_interval,
eval_explorer=explorer,
global_step_hooks=[lr_decay_hook])
def main():
# This prevents numpy from using multiple threads
os.environ['OMP_NUM_THREADS'] = '1'
import logging
# logging.basicConfig(level=logging.DEBUG)
parser = argparse.ArgumentParser()
parser.add_argument('processes', type=int)
parser.add_argument('rom', type=str)
parser.add_argument('--seed', type=int, default=None)
parser.add_argument('--lr', type=float, default=7e-4)
parser.add_argument('--steps', type=int, default=8 * 10**7)
parser.add_argument('--use-sdl', action='store_true', default=False)
parser.add_argument('--final-exploration-frames',
type=int,
default=4 * 10**6)
parser.add_argument('--outdir', type=str, default='nsq_output')
parser.add_argument('--profile', action='store_true')
parser.add_argument('--eval-interval', type=int, default=10**6)
parser.add_argument('--eval-n-runs', type=int, default=10)
parser.add_argument('--demo', action='store_true', default=False)
parser.add_argument('--load', type=str, default=None)
args = parser.parse_args()
if args.seed is not None:
misc.set_random_seed(args.seed)
args.outdir = experiments.prepare_output_dir(args, args.outdir)
print('Output files are saved in {}'.format(args.outdir))
def make_env(process_idx, test):
env = ale.ALE(args.rom,
use_sdl=args.use_sdl,
treat_life_lost_as_terminal=not test)
if not test:
misc.env_modifiers.make_reward_clipped(env, -1, 1)
return env
sample_env = make_env(0, test=False)
action_space = sample_env.action_space
assert isinstance(action_space, spaces.Discrete)
# Define a model and its optimizer
q_func = links.Sequence(links.NIPSDQNHead(), L.Linear(256, action_space.n),
DiscreteActionValue)
opt = rmsprop_async.RMSpropAsync(lr=args.lr, eps=1e-1, alpha=0.99)
opt.setup(q_func)
# Make process-specific agents to diversify exploration
def make_agent(process_idx):
# Random epsilon assignment described in the original paper
rand = random.random()
if rand < 0.4:
epsilon_target = 0.1
elif rand < 0.7:
epsilon_target = 0.01
else:
epsilon_target = 0.5
explorer = explorers.LinearDecayEpsilonGreedy(
1, epsilon_target, args.final_exploration_frames,
action_space.sample)
# Suppress the explorer logger
explorer.logger.setLevel(logging.INFO)
return nsq.NSQ(q_func,
opt,
t_max=5,
gamma=0.99,
i_target=40000,
explorer=explorer,
phi=dqn_phi)
if args.demo:
env = make_env(0, True)
agent = make_agent(0)
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:
explorer = explorers.ConstantEpsilonGreedy(0.05, action_space.sample)
# Linearly decay the learning rate to zero
def lr_setter(env, agent, value):
agent.optimizer.lr = value
lr_decay_hook = experiments.LinearInterpolationHook(
args.steps, args.lr, 0, lr_setter)
experiments.train_agent_async(outdir=args.outdir,
processes=args.processes,
make_env=make_env,
make_agent=make_agent,
profile=args.profile,
steps=args.steps,
eval_n_runs=args.eval_n_runs,
eval_interval=args.eval_interval,
eval_explorer=explorer,
global_step_hooks=[lr_decay_hook])
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--env', type=str, default='BreakoutNoFrameskip-v4')
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=0,
help='Random seed [0, 2 ** 31)')
parser.add_argument('--gpu', type=int, default=0)
parser.add_argument('--demo', action='store_true', default=False)
parser.add_argument('--load', type=str, default=None)
parser.add_argument('--use-sdl', action='store_true', default=False)
parser.add_argument('--final-exploration-frames', type=int, default=10**6)
parser.add_argument('--final-epsilon', type=float, default=0.1)
parser.add_argument('--eval-epsilon', type=float, default=0.05)
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('--replay-start-size', type=int, default=5 * 10**4)
parser.add_argument('--target-update-interval', type=int, default=10**4)
parser.add_argument('--eval-interval', type=int, default=10**5)
parser.add_argument('--update-interval', type=int, default=4)
parser.add_argument('--eval-n-runs', type=int, default=10)
parser.add_argument('--batch-size', type=int, default=32)
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.')
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:
misc.env_modifiers.make_rendered(env)
return env
env = make_env(test=False)
eval_env = make_env(test=True)
n_actions = env.action_space.n
n_atoms = 51
v_max = 10
v_min = -10
q_func = chainerrl.links.Sequence(
chainerrl.links.NatureDQNHead(),
chainerrl.q_functions.DistributionalFCStateQFunctionWithDiscreteAction(
None,
n_actions,
n_atoms,
v_min,
v_max,
n_hidden_channels=0,
n_hidden_layers=0),
)
# Draw the computational graph and save it in the output directory.
chainerrl.misc.draw_computational_graph(
[q_func(np.zeros((4, 84, 84), dtype=np.float32)[None])],
os.path.join(args.outdir, 'model'))
# Use the same hyper parameters as https://arxiv.org/abs/1707.06887
opt = chainer.optimizers.Adam(2.5e-4, eps=1e-2 / args.batch_size)
opt.setup(q_func)
rbuf = replay_buffer.ReplayBuffer(10**6)
explorer = explorers.LinearDecayEpsilonGreedy(
1.0, args.final_epsilon, args.final_exploration_frames,
lambda: np.random.randint(n_actions))
def phi(x):
# Feature extractor
return np.asarray(x, dtype=np.float32) / 255
agent = chainerrl.agents.CategoricalDQN(
q_func,
opt,
rbuf,
gpu=args.gpu,
gamma=0.99,
explorer=explorer,
replay_start_size=args.replay_start_size,
target_update_interval=args.target_update_interval,
update_interval=args.update_interval,
batch_accumulator='mean',
phi=phi,
)
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:
# In testing DQN, randomly select 5% of actions
eval_explorer = explorers.ConstantEpsilonGreedy(
args.eval_epsilon, lambda: np.random.randint(n_actions))
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_explorer=eval_explorer,
save_best_so_far_agent=False,
max_episode_len=args.max_episode_len,
eval_env=eval_env,
)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('rom', type=str)
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=0,
help='Random seed [0, 2 ** 31)')
parser.add_argument('--gpu', type=int, default=0)
parser.add_argument('--demo', action='store_true', default=False)
parser.add_argument('--load', type=str, default=None)
parser.add_argument('--use-sdl', action='store_true', default=False)
parser.add_argument('--final-exploration-frames', type=int, default=10**6)
parser.add_argument('--final-epsilon', type=float, default=0.1)
parser.add_argument('--eval-epsilon', type=float, default=0.05)
parser.add_argument('--arch',
type=str,
default='nature',
choices=['nature', 'nips', 'dueling'])
parser.add_argument('--steps', type=int, default=10**7)
parser.add_argument('--replay-start-size', type=int, default=5 * 10**4)
parser.add_argument('--target-update-interval', type=int, default=10**4)
parser.add_argument('--eval-interval', type=int, default=10**5)
parser.add_argument('--update-interval', type=int, default=4)
parser.add_argument('--activation', type=str, default='relu')
parser.add_argument('--eval-n-runs', type=int, default=10)
parser.add_argument('--no-clip-delta',
dest='clip_delta',
action='store_false')
parser.set_defaults(clip_delta=True)
parser.add_argument('--agent',
type=str,
default='DQN',
choices=['DQN', 'DoubleDQN', 'PAL'])
parser.add_argument('--logging-level',
type=int,
default=20,
help='Logging level. 10:DEBUG, 20:INFO etc.')
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))
# In training, life loss is considered as terminal states
env = ale.ALE(args.rom, use_sdl=args.use_sdl, seed=train_seed)
misc.env_modifiers.make_reward_clipped(env, -1, 1)
# In testing, an episode is terminated when all lives are lost
eval_env = ale.ALE(args.rom,
use_sdl=args.use_sdl,
treat_life_lost_as_terminal=False,
seed=test_seed)
n_actions = env.number_of_actions
activation = parse_activation(args.activation)
q_func = parse_arch(args.arch, n_actions, activation)
# Draw the computational graph and save it in the output directory.
chainerrl.misc.draw_computational_graph(
[q_func(np.zeros((4, 84, 84), dtype=np.float32)[None])],
os.path.join(args.outdir, 'model'))
# Use the same hyper parameters as the Nature paper's
opt = optimizers.RMSpropGraves(lr=2.5e-4,
alpha=0.95,
momentum=0.0,
eps=1e-2)
opt.setup(q_func)
rbuf = replay_buffer.ReplayBuffer(10**6)
explorer = explorers.LinearDecayEpsilonGreedy(
1.0, args.final_epsilon, args.final_exploration_frames,
lambda: np.random.randint(n_actions))
Agent = parse_agent(args.agent)
agent = Agent(q_func,
opt,
rbuf,
gpu=args.gpu,
gamma=0.99,
explorer=explorer,
replay_start_size=args.replay_start_size,
target_update_interval=args.target_update_interval,
clip_delta=args.clip_delta,
update_interval=args.update_interval,
batch_accumulator='sum',
phi=dqn_phi)
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:
# In testing DQN, randomly select 5% of actions
eval_explorer = explorers.ConstantEpsilonGreedy(
args.eval_epsilon, lambda: np.random.randint(n_actions))
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_explorer=eval_explorer,
save_best_so_far_agent=False,
eval_env=eval_env)
def main():
import logging
logging.basicConfig(level=logging.DEBUG)
parser = argparse.ArgumentParser()
parser.add_argument('rom', type=str)
parser.add_argument('--outdir', type=str, default=None)
parser.add_argument('--seed', type=int, default=None)
parser.add_argument('--gpu', type=int, default=0)
parser.add_argument('--demo', action='store_true', default=False)
parser.add_argument('--load', type=str, default=None)
parser.add_argument('--use-sdl', action='store_true', default=False)
parser.add_argument('--final-exploration-frames',
type=int, default=10 ** 6)
parser.add_argument('--model', type=str, default='')
parser.add_argument('--arch', type=str, default='nature',
choices=['nature', 'nips', 'dueling'])
parser.add_argument('--steps', type=int, default=10 ** 7)
parser.add_argument('--replay-start-size', type=int, default=5 * 10 ** 4)
parser.add_argument('--target-update-frequency',
type=int, default=10 ** 4)
parser.add_argument('--eval-frequency', type=int, default=10 ** 5)
parser.add_argument('--update-frequency', type=int, default=4)
parser.add_argument('--activation', type=str, default='relu')
parser.add_argument('--eval-n-runs', type=int, default=10)
parser.add_argument('--no-clip-delta',
dest='clip_delta', action='store_false')
parser.set_defaults(clip_delta=True)
parser.add_argument('--agent', type=str, default='DQN',
choices=['DQN', 'DoubleDQN', 'PAL'])
args = parser.parse_args()
if args.seed is not None:
misc.set_random_seed(args.seed)
args.outdir = experiments.prepare_output_dir(args, args.outdir)
print('Output files are saved in {}'.format(args.outdir))
# In training, life loss is considered as terminal states
env = ale.ALE(args.rom, use_sdl=args.use_sdl)
misc.env_modifiers.make_reward_clipped(env, -1, 1)
# In testing, an episode is terminated when all lives are lost
eval_env = ale.ALE(args.rom, use_sdl=args.use_sdl,
treat_life_lost_as_terminal=False)
n_actions = env.number_of_actions
activation = parse_activation(args.activation)
q_func = parse_arch(args.arch, n_actions, activation)
# Use the same hyper parameters as the Nature paper's
opt = optimizers.RMSpropGraves(
lr=2.5e-4, alpha=0.95, momentum=0.0, eps=1e-2)
opt.setup(q_func)
rbuf = replay_buffer.ReplayBuffer(10 ** 6)
explorer = explorers.LinearDecayEpsilonGreedy(
1.0, 0.1,
args.final_exploration_frames,
lambda: np.random.randint(n_actions))
Agent = parse_agent(args.agent)
agent = Agent(q_func, opt, rbuf, gpu=args.gpu, gamma=0.99,
explorer=explorer, replay_start_size=args.replay_start_size,
target_update_frequency=args.target_update_frequency,
clip_delta=args.clip_delta,
update_frequency=args.update_frequency,
batch_accumulator='sum', phi=dqn_phi)
if args.load:
agent.load(args.load)
if args.demo:
mean, median, stdev = 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, mean, median, stdev))
else:
# In testing DQN, randomly select 5% of actions
eval_explorer = explorers.ConstantEpsilonGreedy(
5e-2, lambda: np.random.randint(n_actions))
experiments.train_agent_with_evaluation(
agent=agent, env=env, steps=args.steps,
eval_n_runs=args.eval_n_runs, eval_frequency=args.eval_frequency,
outdir=args.outdir, eval_explorer=eval_explorer,
eval_env=eval_env)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('processes', type=int)
parser.add_argument('--env', type=str, default='BreakoutNoFrameskip-v4')
parser.add_argument('--seed',
type=int,
default=0,
help='Random seed [0, 2 ** 31)')
parser.add_argument('--lr', type=float, default=7e-4)
parser.add_argument('--steps', type=int, default=8 * 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('--final-exploration-frames',
type=int,
default=4 * 10**6)
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('--profile', action='store_true')
parser.add_argument('--eval-interval', type=int, default=10**6)
parser.add_argument('--eval-n-runs', type=int, default=10)
parser.add_argument('--demo', action='store_true', default=False)
parser.add_argument('--load', type=str, default=None)
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.')
args = parser.parse_args()
import logging
logging.basicConfig(level=args.logging_level)
# Set a random seed used in ChainerRL.
# If you use more than one processes, the results will be no longer
# deterministic even with the same random seed.
misc.set_random_seed(args.seed)
# Set different random seeds for different subprocesses.
# If seed=0 and processes=4, subprocess seeds are [0, 1, 2, 3].
# If seed=1 and processes=4, subprocess seeds are [4, 5, 6, 7].
process_seeds = np.arange(args.processes) + args.seed * args.processes
assert process_seeds.max() < 2**31
args.outdir = experiments.prepare_output_dir(args, args.outdir)
print('Output files are saved in {}'.format(args.outdir))
def make_env(process_idx, test):
# Use different random seeds for train and test envs
process_seed = process_seeds[process_idx]
env_seed = 2**31 - 1 - process_seed if test else process_seed
env = atari_wrappers.wrap_deepmind(atari_wrappers.make_atari(args.env),
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:
misc.env_modifiers.make_rendered(env)
return env
sample_env = make_env(0, test=False)
action_space = sample_env.action_space
assert isinstance(action_space, spaces.Discrete)
# Define a model and its optimizer
q_func = links.Sequence(links.NIPSDQNHead(), L.Linear(256, action_space.n),
DiscreteActionValue)
opt = rmsprop_async.RMSpropAsync(lr=args.lr, eps=1e-1, alpha=0.99)
opt.setup(q_func)
def phi(x):
# Feature extractor
return np.asarray(x, dtype=np.float32) / 255
# Make process-specific agents to diversify exploration
def make_agent(process_idx):
# Random epsilon assignment described in the original paper
rand = random.random()
if rand < 0.4:
epsilon_target = 0.1
elif rand < 0.7:
epsilon_target = 0.01
else:
epsilon_target = 0.5
explorer = explorers.LinearDecayEpsilonGreedy(
1, epsilon_target, args.final_exploration_frames,
action_space.sample)
# Suppress the explorer logger
explorer.logger.setLevel(logging.INFO)
return nsq.NSQ(q_func,
opt,
t_max=5,
gamma=0.99,
i_target=40000,
explorer=explorer,
phi=phi)
if args.demo:
env = make_env(0, True)
agent = make_agent(0)
eval_stats = experiments.eval_performance(env=env,
agent=agent,
n_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:
explorer = explorers.ConstantEpsilonGreedy(0.05, action_space.sample)
# Linearly decay the learning rate to zero
def lr_setter(env, agent, value):
agent.optimizer.lr = value
lr_decay_hook = experiments.LinearInterpolationHook(
args.steps, args.lr, 0, lr_setter)
experiments.train_agent_async(
outdir=args.outdir,
processes=args.processes,
make_env=make_env,
make_agent=make_agent,
profile=args.profile,
steps=args.steps,
eval_n_runs=args.eval_n_runs,
eval_interval=args.eval_interval,
eval_explorer=explorer,
max_episode_len=args.max_episode_len,
global_step_hooks=[lr_decay_hook],
save_best_so_far_agent=False,
)
'adam': optimizers.Adam(alpha=1e-3),
'adadelta': optimizers.AdaDelta(rho=0.95)
}
available_explorers = {
'boltzmann':
explorers.Boltzmann(T=1.0),
'lin_decay_eps_greedy':
explorers.LinearDecayEpsilonGreedy(start_epsilon=1.0,
end_epsilon=0.0,
decay_steps=10,
random_action_func=None,
logger=None),
'const_eps_greedy':
explorers.ConstantEpsilonGreedy(epsilon=1.0,
random_action_func=None,
logger=None)
}
class BaseAgent(object):
def __init__(self,
env,
feature_transformer,
gamma=0.99,
optimizer='adadelta',
explorer='boltzmann'):
self.actions = dict([(a, i)
for (i, a) in enumerate(env.actions_available)])
self.n_actions = len(self.actions)
self.n_dims = feature_transformer.dimensions
