def learn(policy, env, seed, nsteps=20, nstack=4, total_timesteps=int(80e6), q_coef=0.5, ent_coef=0.01,
max_grad_norm=10, lr=7e-4, lrschedule='linear', rprop_epsilon=1e-5, rprop_alpha=0.99, gamma=0.99,
log_interval=100, buffer_size=50000, replay_ratio=4, replay_start=10000, c=10.0,
trust_region=True, alpha=0.99, delta=1):
print("Running Acer Simple")
print(locals())
tf.reset_default_graph()
set_global_seeds(seed)
nenvs = env.num_envs
ob_space = env.observation_space
ac_space = env.action_space
num_procs = len(env.remotes) # HACK
model = Model(policy=policy, ob_space=ob_space, ac_space=ac_space, nenvs=nenvs, nsteps=nsteps, nstack=nstack,
num_procs=num_procs, ent_coef=ent_coef, q_coef=q_coef, gamma=gamma,
max_grad_norm=max_grad_norm, lr=lr, rprop_alpha=rprop_alpha, rprop_epsilon=rprop_epsilon,
total_timesteps=total_timesteps, lrschedule=lrschedule, c=c,
trust_region=trust_region, alpha=alpha, delta=delta)
runner = Runner(env=env, model=model, nsteps=nsteps, nstack=nstack)
if replay_ratio > 0:
buffer = Buffer(env=env, nsteps=nsteps, nstack=nstack, size=buffer_size)
else:
buffer = None
nbatch = nenvs*nsteps
acer = Acer(runner, model, buffer, log_interval)
acer.tstart = time.time()
for acer.steps in range(0, total_timesteps, nbatch): #nbatch samples, 1 on_policy call and multiple off-policy calls
acer.call(on_policy=True)
if replay_ratio > 0 and buffer.has_atleast(replay_start):
n = np.random.poisson(replay_ratio)
for _ in range(n):
acer.call(on_policy=False) # no simulation steps in this
env.close()
def learn(policy, env, seed, nsteps=20, nstack=4, total_timesteps=int(80e6), q_coef=0.5, ent_coef=0.01,
max_grad_norm=10, lr=7e-4, lrschedule='linear', rprop_epsilon=1e-5, rprop_alpha=0.99, gamma=0.99,
log_interval=100, buffer_size=50000, replay_ratio=4, replay_start=10000, c=10.0,
trust_region=True, alpha=0.99, delta=1):
print("Running Acer Simple")
print(locals())
tf.reset_default_graph()
set_global_seeds(seed)
nenvs = env.num_envs
ob_space = env.observation_space
ac_space = env.action_space
num_procs = len(env.remotes) # HACK
model = Model(policy=policy, ob_space=ob_space, ac_space=ac_space, nenvs=nenvs, nsteps=nsteps, nstack=nstack,
num_procs=num_procs, ent_coef=ent_coef, q_coef=q_coef, gamma=gamma,
max_grad_norm=max_grad_norm, lr=lr, rprop_alpha=rprop_alpha, rprop_epsilon=rprop_epsilon,
total_timesteps=total_timesteps, lrschedule=lrschedule, c=c,
trust_region=trust_region, alpha=alpha, delta=delta)
runner = Runner(env=env, model=model, nsteps=nsteps, nstack=nstack)
if replay_ratio > 0:
buffer = Buffer(env=env, nsteps=nsteps, nstack=nstack, size=buffer_size)
else:
buffer = None
nbatch = nenvs*nsteps
acer = Acer(runner, model, buffer, log_interval)
acer.tstart = time.time()
for acer.steps in range(0, total_timesteps, nbatch): #nbatch samples, 1 on_policy call and multiple off-policy calls
acer.call(on_policy=True)
if replay_ratio > 0 and buffer.has_atleast(replay_start):
n = np.random.poisson(replay_ratio)
for _ in range(n):
acer.call(on_policy=False) # no simulation steps in this
env.close()
def learn(network, env, seed=None, nsteps=20, total_timesteps=int(80e6), q_coef=0.5, ent_coef=0.01,
max_grad_norm=10, lr=7e-4, lrschedule='linear', rprop_epsilon=1e-5, rprop_alpha=0.99, gamma=0.99,
log_interval=100, buffer_size=50000, replay_ratio=4, replay_start=10000, c=10.0,
trust_region=True, alpha=0.99, delta=1, load_path=None, **network_kwargs):
'''
Main entrypoint for ACER (Actor-Critic with Experience Replay) algorithm (https://arxiv.org/pdf/1611.01224.pdf)
Train an agent with given network architecture on a given environment using ACER.
Parameters:
----------
network: policy network architecture. Either string (mlp, lstm, lnlstm, cnn_lstm, cnn, cnn_small, conv_only - see baselines.common/models.py for full list)
specifying the standard network architecture, or a function that takes tensorflow tensor as input and returns
tuple (output_tensor, extra_feed) where output tensor is the last network layer output, extra_feed is None for feed-forward
neural nets, and extra_feed is a dictionary describing how to feed state into the network for recurrent neural nets.
See baselines.common/policies.py/lstm for more details on using recurrent nets in policies
env: environment. Needs to be vectorized for parallel environment simulation.
The environments produced by gym.make can be wrapped using baselines.common.vec_env.DummyVecEnv class.
nsteps: int, number of steps of the vectorized environment per update (i.e. batch size is nsteps * nenv where
nenv is number of environment copies simulated in parallel) (default: 20)
nstack: int, size of the frame stack, i.e. number of the frames passed to the step model. Frames are stacked along channel dimension
(last image dimension) (default: 4)
total_timesteps: int, number of timesteps (i.e. number of actions taken in the environment) (default: 80M)
q_coef: float, value function loss coefficient in the optimization objective (analog of vf_coef for other actor-critic methods)
ent_coef: float, policy entropy coefficient in the optimization objective (default: 0.01)
max_grad_norm: float, gradient norm clipping coefficient. If set to None, no clipping. (default: 10),
lr: float, learning rate for RMSProp (current implementation has RMSProp hardcoded in) (default: 7e-4)
lrschedule: schedule of learning rate. Can be 'linear', 'constant', or a function [0..1] -> [0..1] that takes fraction of the training progress as input and
returns fraction of the learning rate (specified as lr) as output
rprop_epsilon: float, RMSProp epsilon (stabilizes square root computation in denominator of RMSProp update) (default: 1e-5)
rprop_alpha: float, RMSProp decay parameter (default: 0.99)
gamma: float, reward discounting factor (default: 0.99)
log_interval: int, number of updates between logging events (default: 100)
buffer_size: int, size of the replay buffer (default: 50k)
replay_ratio: int, now many (on average) batches of data to sample from the replay buffer take after batch from the environment (default: 4)
replay_start: int, the sampling from the replay buffer does not start until replay buffer has at least that many samples (default: 10k)
c: float, importance weight clipping factor (default: 10)
trust_region bool, whether or not algorithms estimates the gradient KL divergence between the old and updated policy and uses it to determine step size (default: True)
delta: float, max KL divergence between the old policy and updated policy (default: 1)
alpha: float, momentum factor in the Polyak (exponential moving average) averaging of the model parameters (default: 0.99)
load_path: str, path to load the model from (default: None)
**network_kwargs: keyword arguments to the policy / network builder. See baselines.common/policies.py/build_policy and arguments to a particular type of network
For instance, 'mlp' network architecture has arguments num_hidden and num_layers.
'''
print("Running Acer Simple")
print(locals())
set_global_seeds(seed)
if not isinstance(env, VecFrameStack):
env = VecFrameStack(env, 1)
policy = build_policy(env, network, estimate_q=True, **network_kwargs)
nenvs = env.num_envs
ob_space = env.observation_space
ac_space = env.action_space
nstack = env.nstack
model = Model(policy=policy, ob_space=ob_space, ac_space=ac_space, nenvs=nenvs, nsteps=nsteps,
ent_coef=ent_coef, q_coef=q_coef, gamma=gamma,
max_grad_norm=max_grad_norm, lr=lr, rprop_alpha=rprop_alpha, rprop_epsilon=rprop_epsilon,
total_timesteps=total_timesteps, lrschedule=lrschedule, c=c,
trust_region=trust_region, alpha=alpha, delta=delta)
runner = Runner(env=env, model=model, nsteps=nsteps)
if replay_ratio > 0:
buffer = Buffer(env=env, nsteps=nsteps, size=buffer_size)
else:
buffer = None
nbatch = nenvs*nsteps
acer = Acer(runner, model, buffer, log_interval)
acer.tstart = time.time()
for acer.steps in range(0, total_timesteps, nbatch): #nbatch samples, 1 on_policy call and multiple off-policy calls
acer.call(on_policy=True)
if replay_ratio > 0 and buffer.has_atleast(replay_start):
n = np.random.poisson(replay_ratio)
for _ in range(n):
acer.call(on_policy=False) # no simulation steps in this
return model
def learn(policy, env, flags):
"""
:param policy:
:param baselines.common.vec_env.VecEnv env:
:param baselines.acer.flags.AcerFlags flags:
"""
print("Running Acer Simple")
print(flags)
flags.total_timesteps = int(flags.total_timesteps)
# disable gpu before creating any tensor
if not flags.use_gpu:
tf_util.disable_gpu()
tf.reset_default_graph()
set_global_seeds(flags.seed)
nenvs = env.num_envs
ob_space = env.observation_space
ac_space = env.action_space
model = Model(policy=policy, ob_space=ob_space, ac_space=ac_space, nenvs=nenvs, num_procs=nenvs, flags=flags)
runner = Runner(env=env, model=model, nsteps=flags.nsteps, nstack=flags.nstack)
if flags.replay_ratio > 0:
buffer = Buffer(env=env, nsteps=flags.nsteps, nstack=flags.nstack, size=flags.buffer_size)
else:
buffer = None
nbatch = nenvs*flags.nsteps
acer = Acer(runner, model, buffer, flags.log_interval, flags.stats_interval)
saver = tf.train.Saver(max_to_keep=3, keep_checkpoint_every_n_hours=flags.permanent_save_hours)
checkpoint_dir = os.path.join(flags.save_dir, 'checkpoints')
checkpoint_path = os.path.join(checkpoint_dir, 'model')
os.makedirs(checkpoint_dir, exist_ok=True)
# load checkpoint
latest_checkpoint = tf.train.latest_checkpoint(checkpoint_dir)
if latest_checkpoint:
print("Loading model checkpoint: {}".format(latest_checkpoint))
saver.restore(model.sess, latest_checkpoint)
start_steps = model.GSwrapper.get(model.sess)
if hasattr(env, 'restore_state'):
env.restore_state(checkpoint_dir, start_steps)
else:
start_steps = 0
coordinator = tf.train.Coordinator()
def signal_handler(signal, frame):
if not coordinator.should_stop():
coordinator.request_stop()
print("Stopping training...")
else:
print("Stop already requested, please wait...")
signal.signal(signal.SIGINT, signal_handler)
print("Press CTRL+C to stop")
acer.tstart = time.time()
for acer.steps in range(start_steps, flags.total_timesteps, nbatch):
# on policy training
acer.call(on_policy=True)
# off policy training
if flags.replay_ratio > 0 and buffer.has_atleast(flags.replay_start):
n = np.random.poisson(flags.replay_ratio)
for _ in range(n):
acer.call(on_policy=False) # no simulation steps in this
# saving
do_save = (((acer.steps//nbatch) + 1) % flags.save_interval == 0) or coordinator.should_stop()
if do_save:
save_steps = acer.steps+nbatch
print("Saving at t=%s" % save_steps)
model.GSwrapper.set(model.sess, save_steps)
saver.save(model.sess, save_path=checkpoint_path, global_step=save_steps)
if hasattr(env, 'save_state'):
env.save_state(checkpoint_dir, save_steps)
if coordinator.should_stop():
break
env.close()
def learn(network, env, seed=None, nsteps=20, total_timesteps=int(80e6), q_coef=0.5, ent_coef=0.01,
max_grad_norm=10, lr=7e-4, lrschedule='linear', rprop_epsilon=1e-5, rprop_alpha=0.99, gamma=0.99,
log_interval=100, buffer_size=50000, replay_ratio=4, replay_start=10000, c=10.0,
trust_region=True, alpha=0.99, delta=1, load_path=None, **network_kwargs):
'''
Main entrypoint for ACER (Actor-Critic with Experience Replay) algorithm (https://arxiv.org/pdf/1611.01224.pdf)
Train an agent with given network architecture on a given environment using ACER.
Parameters:
----------
network: policy network architecture. Either string (mlp, lstm, lnlstm, cnn_lstm, cnn, cnn_small, conv_only - see baselines.common/models.py for full list)
specifying the standard network architecture, or a function that takes tensorflow tensor as input and returns
tuple (output_tensor, extra_feed) where output tensor is the last network layer output, extra_feed is None for feed-forward
neural nets, and extra_feed is a dictionary describing how to feed state into the network for recurrent neural nets.
See baselines.common/policies.py/lstm for more details on using recurrent nets in policies
env: environment. Needs to be vectorized for parallel environment simulation.
The environments produced by gym.make can be wrapped using baselines.common.vec_env.DummyVecEnv class.
nsteps: int, number of steps of the vectorized environment per update (i.e. batch size is nsteps * nenv where
nenv is number of environment copies simulated in parallel) (default: 20)
nstack: int, size of the frame stack, i.e. number of the frames passed to the step model. Frames are stacked along channel dimension
(last image dimension) (default: 4)
total_timesteps: int, number of timesteps (i.e. number of actions taken in the environment) (default: 80M)
q_coef: float, value function loss coefficient in the optimization objective (analog of vf_coef for other actor-critic methods)
ent_coef: float, policy entropy coefficient in the optimization objective (default: 0.01)
max_grad_norm: float, gradient norm clipping coefficient. If set to None, no clipping. (default: 10),
lr: float, learning rate for RMSProp (current implementation has RMSProp hardcoded in) (default: 7e-4)
lrschedule: schedule of learning rate. Can be 'linear', 'constant', or a function [0..1] -> [0..1] that takes fraction of the training progress as input and
returns fraction of the learning rate (specified as lr) as output
rprop_epsilon: float, RMSProp epsilon (stabilizes square root computation in denominator of RMSProp update) (default: 1e-5)
rprop_alpha: float, RMSProp decay parameter (default: 0.99)
gamma: float, reward discounting factor (default: 0.99)
log_interval: int, number of updates between logging events (default: 100)
buffer_size: int, size of the replay buffer (default: 50k)
replay_ratio: int, now many (on average) batches of data to sample from the replay buffer take after batch from the environment (default: 4)
replay_start: int, the sampling from the replay buffer does not start until replay buffer has at least that many samples (default: 10k)
c: float, importance weight clipping factor (default: 10)
trust_region bool, whether or not algorithms estimates the gradient KL divergence between the old and updated policy and uses it to determine step size (default: True)
delta: float, max KL divergence between the old policy and updated policy (default: 1)
alpha: float, momentum factor in the Polyak (exponential moving average) averaging of the model parameters (default: 0.99)
load_path: str, path to load the model from (default: None)
**network_kwargs: keyword arguments to the policy / network builder. See baselines.common/policies.py/build_policy and arguments to a particular type of network
For instance, 'mlp' network architecture has arguments num_hidden and num_layers.
'''
print("Running Acer Simple")
print(locals())
set_global_seeds(seed)
if not isinstance(env, VecFrameStack):
env = VecFrameStack(env, 1)
policy = build_policy(env, network, estimate_q=True, **network_kwargs)
nenvs = env.num_envs
ob_space = env.observation_space
ac_space = env.action_space
nstack = env.nstack
model = Model(policy=policy, ob_space=ob_space, ac_space=ac_space, nenvs=nenvs, nsteps=nsteps,
ent_coef=ent_coef, q_coef=q_coef, gamma=gamma,
max_grad_norm=max_grad_norm, lr=lr, rprop_alpha=rprop_alpha, rprop_epsilon=rprop_epsilon,
total_timesteps=total_timesteps, lrschedule=lrschedule, c=c,
trust_region=trust_region, alpha=alpha, delta=delta)
runner = Runner(env=env, model=model, nsteps=nsteps)
if replay_ratio > 0:
buffer = Buffer(env=env, nsteps=nsteps, size=buffer_size)
else:
buffer = None
nbatch = nenvs*nsteps
acer = Acer(runner, model, buffer, log_interval)
acer.tstart = time.time()
for acer.steps in range(0, total_timesteps, nbatch): #nbatch samples, 1 on_policy call and multiple off-policy calls
acer.call(on_policy=True)
if replay_ratio > 0 and buffer.has_atleast(replay_start):
n = np.random.poisson(replay_ratio)
for _ in range(n):
acer.call(on_policy=False) # no simulation steps in this
return model
def learn(policy,
env,
seed,
env_id,
learn_time,
expert_buffer_size,
perform=False,
use_expert=False,
save_networks=False,
network_saving_dir=None,
total_timesteps=int(80e6),
nsteps=20,
nstack=4,
q_coef=0.5,
ent_coef=0.01,
max_grad_norm=10,
lr=7e-4,
lrschedule='linear',
rprop_epsilon=1e-5,
rprop_alpha=0.99,
gamma=0.99,
log_interval=10,
buffer_size=50000,
replay_ratio=4,
replay_start=10000,
c=10.0,
trust_region=True,
alpha=0.99,
delta=1):
print(locals())
tf.reset_default_graph()
set_global_seeds(seed)
nenvs = env.num_envs
ob_space = env.observation_space #Box(84,84,1)
ac_space = env.action_space #Discrete(4)
if use_expert:
expert = Expert(
env=env, nsteps=nsteps, nstack=nstack,
size=expert_buffer_size) #Exp1:50000; Exp2:25000 ; Exp3:10000
expert_dir = os.path.join('./expert') + '/expert.pkl'
file_dir = '/home/zhangxiaoqin/Projects/conda/atari_v1/'
#expert.load_file_human(file_dir)
expert.load_file(expert_dir)
else:
expert = None
num_procs = len(env.remotes) # HACK
model = Model(policy=policy,
ob_space=ob_space,
ac_space=ac_space,
nenvs=nenvs,
nsteps=nsteps,
nstack=nstack,
num_procs=num_procs,
ent_coef=ent_coef,
q_coef=q_coef,
gamma=gamma,
max_grad_norm=max_grad_norm,
lr=lr,
rprop_alpha=rprop_alpha,
rprop_epsilon=rprop_epsilon,
total_timesteps=total_timesteps,
lrschedule=lrschedule,
c=c,
trust_region=trust_region,
alpha=alpha,
delta=delta,
network_saving_dir=network_saving_dir,
use_expert=use_expert,
expert=expert)
runner = Runner(env=env,
model=model,
nsteps=nsteps,
nstack=nstack,
env_id=env_id)
if replay_ratio > 0:
buffer = Buffer(env=env,
nsteps=nsteps,
nstack=nstack,
size=buffer_size)
else:
buffer = None
if perform:
model.load()
nbatch = nenvs * nsteps
acer = Acer(runner, model, buffer, log_interval)
acer.tstart = time.time()
for acer.steps in range(
0, total_timesteps, nbatch
): #nbatch samples, 1 on_policy call and multiple off-policy calls
if acer.steps > learn_time and use_expert:
print('-------------------------')
print('Reuse the normal networks')
print('-------------------------')
use_expert = False
expert = None
acer.call(perform, save_networks, use_expert, expert, on_policy=True)
if replay_ratio > 0 and buffer.has_atleast(
replay_start) and not perform:
n = np.random.poisson(replay_ratio)
for _ in range(n):
acer.call(perform,
save_networks,
use_expert,
expert,
on_policy=False) # no simulation steps in this
#dir = os.path.join('./models/', 'test.m')
#model.save('./models/test_2.pkl')
#
#
env.close()
def learn(network,
env,
seed=None,
nsteps=20,
total_timesteps=int(80e6),
q_coef=0.5,
ent_coef=0.01,
max_grad_norm=10,
lr=7e-4,
lrschedule='linear',
rprop_epsilon=1e-5,
rprop_alpha=0.99,
gamma=0.99,
log_interval=100,
buffer_size=50000,
replay_ratio=4,
replay_start=10000,
c=10.0,
trust_region=True,
alpha=0.99,
delta=1,
load_path=None,
**network_kwargs):
'''
Main entrypoint for ACER (Actor-Critic with Experience Replay) algorithm (https://arxiv.org/pdf/1611.01224.pdf)
Train an agent with given network architecture on a given environment using ACER.
Parameters:
----------
network: policy network architecture. Either string (mlp, lstm, lnlstm, cnn_lstm, cnn, cnn_small, conv_only - see baselines.common/models.py for full list)
specifying the standard network architecture, or a function that takes tensorflow tensor as input and returns
tuple (output_tensor, extra_feed) where output tensor is the last network layer output, extra_feed is None for feed-forward
neural nets, and extra_feed is a dictionary describing how to feed state into the network for recurrent neural nets.
See baselines.common/policies.py/lstm for more details on using recurrent nets in policies
env: environment. Needs to be vectorized for parallel environment simulation.
The environments produced by gym.make can be wrapped using baselines.common.vec_env.DummyVecEnv class.
nsteps: int, number of steps of the vectorized environment per update (i.e. batch size is nsteps * nenv where
nenv is number of environment copies simulated in parallel) (default: 20)
nstack: int, size of the frame stack, i.e. number of the frames passed to the step model. Frames are stacked along channel dimension
(last image dimension) (default: 4)
total_timesteps: int, number of timesteps (i.e. number of actions taken in the environment) (default: 80M)
q_coef: float, value function loss coefficient in the optimization objective (analog of vf_coef for other actor-critic methods)
ent_coef: float, policy entropy coefficient in the optimization objective (default: 0.01)
max_grad_norm: float, gradient norm clipping coefficient. If set to None, no clipping. (default: 10),
lr: float, learning rate for RMSProp (current implementation has RMSProp hardcoded in) (default: 7e-4)
lrschedule: schedule of learning rate. Can be 'linear', 'constant', or a function [0..1] -> [0..1] that takes fraction of the training progress as input and
returns fraction of the learning rate (specified as lr) as output
rprop_epsilon: float, RMSProp epsilon (stabilizes square root computation in denominator of RMSProp update) (default: 1e-5)
rprop_alpha: float, RMSProp decay parameter (default: 0.99)
gamma: float, reward discounting factor (default: 0.99)
log_interval: int, number of updates between logging events (default: 100)
buffer_size: int, size of the replay buffer (default: 50k)
replay_ratio: int, now many (on average) batches of data to sample from the replay buffer take after batch from the environment (default: 4)
replay_start: int, the sampling from the replay buffer does not start until replay buffer has at least that many samples (default: 10k)
c: float, importance weight clipping factor (default: 10)
trust_region bool, whether or not algorithms estimates the gradient KL divergence between the old and updated policy and uses it to determine step size (default: True)
delta: float, max KL divergence between the old policy and updated policy (default: 1)
alpha: float, momentum factor in the Polyak (exponential moving average) averaging of the model parameters (default: 0.99)
load_path: str, path to load the model from (default: None)
**network_kwargs: keyword arguments to the policy / network builder. See baselines.common/policies.py/build_policy and arguments to a particular type of network
For instance, 'mlp' network architecture has arguments num_hidden and num_layers.
'''
print("Running Acer Simple")
learn_params = {
"network": network,
"seed": seed,
"nsteps": nsteps,
"total_timesteps": total_timesteps,
"q_coef": q_coef,
"ent_coef": ent_coef,
"max_grad_norm": max_grad_norm,
"lr": lr,
"lrschedule": lrschedule,
"rprop_epsilon": rprop_epsilon,
"rprop_alpha": rprop_alpha,
"gamma": gamma,
"log_interval": log_interval,
"buffer_size": buffer_size,
"replay_ratio": replay_ratio,
"replay_start": replay_start,
"c": c,
"trust_region": trust_region,
"alpha": alpha,
"delta": delta,
"load_path": load_path,
**network_kwargs
}
with open("params.json") as f:
params = json.load(f)
params["replay_start"] = min(params["replay_start"], params["buffer_size"])
params["buffer_size"] = min(params["buffer_size"],
params["disk_buffer_size"])
nsteps, buffer_size, disk_buffer_size = params['nsteps'], params[
'buffer_size'], params['disk_buffer_size']
for k, v in params.items():
if k in learn_params:
learn_params[k] = v
# print(locals())
with open("model_params.pkl", "wb") as f:
pkl.dump(learn_params, f)
env, policy, nenvs, ob_space, ac_space, nstack, model = create_model(
**learn_params)
# *** UNCOMMENT IF YOU WANT TO LOAD OLD VARIABLES
load_variables("actor.ckpt")
# ***
# runner = HaliteRunner(model=model, env=env, gamma=gamma, nsteps=nsteps)
runner = HaliteRunner(model) # reads the params json now
if replay_ratio > 0:
buffer = Buffer(env=env,
nsteps=nsteps,
size=buffer_size,
disk_size=disk_buffer_size)
else:
buffer = None
nbatch = nenvs * nsteps
acer = Acer(runner, model, buffer, log_interval, nsteps)
acer.tstart = time.time()
for acer.steps in range(
0, total_timesteps, nbatch
): #nbatch samples, 1 on_policy call and multiple off-policy calls
acer.call(on_policy=True)
if replay_ratio > 0 and buffer.has_atleast(replay_start):
n = replay_ratio #np.random.poisson(replay_ratio)
for _ in range(n):
acer.call(on_policy=False) # no simulation steps in this
return model
def learn(policy,
env,
seed,
n_steps=20,
n_stack=4,
total_timesteps=int(80e6),
q_coef=0.5,
ent_coef=0.01,
max_grad_norm=10,
learning_rate=7e-4,
lr_schedule='linear',
rprop_epsilon=1e-5,
rprop_alpha=0.99,
gamma=0.99,
log_interval=100,
buffer_size=50000,
replay_ratio=4,
replay_start=10000,
correction_term=10.0,
trust_region=True,
alpha=0.99,
delta=1):
"""
Train an ACER model.
:param policy: (ACERPolicy) The policy model to use (MLP, CNN, LSTM, ...)
:param env: (Gym environment) The environment to learn from
:param seed: (int) The initial seed for training
:param n_steps: (int) The number of steps to run for each environment
:param n_stack: (int) The number of stacked frames
:param total_timesteps: (int) The total number of samples
:param q_coef: (float) Q function coefficient for the loss calculation
:param ent_coef: (float) Entropy coefficient for the loss caculation
:param max_grad_norm: (float) The maximum value for the gradient clipping
:param learning_rate: (float) The learning rate
:param lr_schedule: (str) The type of scheduler for the learning rate update ('linear', 'constant',
'double_linear_con', 'middle_drop' or 'double_middle_drop')
:param rprop_epsilon: (float) RMS prop optimizer epsilon
:param rprop_alpha: (float) RMS prop optimizer decay
:param gamma: (float) Discount factor
:param log_interval: (int) The number of timesteps before logging.
:param buffer_size: (int) The buffer size in number of steps
:param replay_ratio: (float) The number of replay learning per on policy learning on average,
using a poisson distribution
:param replay_start: (int) The minimum number of steps in the buffer, before learning replay
:param correction_term: (float) The correction term for the weights
:param trust_region: (bool) Enable Trust region policy optimization loss
:param alpha: (float) The decay rate for the Exponential moving average of the parameters
:param delta: (float) trust region delta value
"""
print("Running Acer Simple")
print(locals())
set_global_seeds(seed)
n_envs = env.num_envs
ob_space = env.observation_space
ac_space = env.action_space
num_procs = len(env.remotes) # HACK
model = Model(policy=policy,
ob_space=ob_space,
ac_space=ac_space,
n_envs=n_envs,
n_steps=n_steps,
n_stack=n_stack,
num_procs=num_procs,
ent_coef=ent_coef,
q_coef=q_coef,
gamma=gamma,
max_grad_norm=max_grad_norm,
learning_rate=learning_rate,
rprop_alpha=rprop_alpha,
rprop_epsilon=rprop_epsilon,
total_timesteps=total_timesteps,
lr_schedule=lr_schedule,
correction_term=correction_term,
trust_region=trust_region,
alpha=alpha,
delta=delta)
runner = Runner(env=env, model=model, n_steps=n_steps, n_stack=n_stack)
if replay_ratio > 0:
buffer = Buffer(env=env,
n_steps=n_steps,
n_stack=n_stack,
size=buffer_size)
else:
buffer = None
n_batch = n_envs * n_steps
acer = Acer(runner, model, buffer, log_interval)
acer.t_start = time.time()
for acer.steps in range(
0, total_timesteps, n_batch
): # n_batch samples, 1 on_policy call and multiple off-policy calls
acer.call(on_policy=True)
if replay_ratio > 0 and buffer.has_atleast(replay_start):
samples_number = np.random.poisson(replay_ratio)
for _ in range(samples_number):
acer.call(on_policy=False) # no simulation steps in this
env.close()
