def make_model(self, env):
n_hidden_channels = 20
obs_size = env.observation_space.low.size
if self.recurrent:
v = StatelessRecurrentSequential(
L.NStepLSTM(1, obs_size, n_hidden_channels, 0),
L.Linear(
None, 1, initialW=chainer.initializers.LeCunNormal(1e-1)),
)
if self.discrete:
n_actions = env.action_space.n
pi = StatelessRecurrentSequential(
L.NStepLSTM(1, obs_size, n_hidden_channels, 0),
policies.FCSoftmaxPolicy(
n_hidden_channels, n_actions,
n_hidden_layers=0,
nonlinearity=F.tanh,
last_wscale=1e-1,
)
)
else:
action_size = env.action_space.low.size
pi = StatelessRecurrentSequential(
L.NStepLSTM(1, obs_size, n_hidden_channels, 0),
policies.FCGaussianPolicy(
n_hidden_channels, action_size,
n_hidden_layers=0,
nonlinearity=F.tanh,
mean_wscale=1e-1,
)
)
return StatelessRecurrentBranched(pi, v)
else:
v = chainer.Sequential(
L.Linear(None, n_hidden_channels),
F.tanh,
L.Linear(
None, 1, initialW=chainer.initializers.LeCunNormal(1e-1)),
)
if self.discrete:
n_actions = env.action_space.n
pi = policies.FCSoftmaxPolicy(
obs_size, n_actions,
n_hidden_layers=1,
n_hidden_channels=n_hidden_channels,
nonlinearity=F.tanh,
last_wscale=1e-1,
)
else:
action_size = env.action_space.low.size
pi = policies.FCGaussianPolicy(
obs_size, action_size,
n_hidden_layers=1,
n_hidden_channels=n_hidden_channels,
nonlinearity=F.tanh,
mean_wscale=1e-1,
)
return A3CSeparateModel(pi=pi, v=v)
def make_model(self, env):
n_hidden_channels = 50
n_dim_obs = env.observation_space.low.size
v = v_functions.FCVFunction(
n_dim_obs,
n_hidden_layers=2,
n_hidden_channels=n_hidden_channels)
if self.discrete:
n_actions = env.action_space.n
pi = policies.FCSoftmaxPolicy(
n_dim_obs, n_actions,
n_hidden_layers=2,
n_hidden_channels=n_hidden_channels)
else:
n_dim_actions = env.action_space.low.size
pi = policies.FCGaussianPolicy(
n_dim_obs, n_dim_actions,
n_hidden_layers=2,
n_hidden_channels=n_hidden_channels)
return A3CSeparateModel(pi=pi, v=v)
def __init__(self,
env,
feature_transformer,
gamma=0.99,
optimizer='adam',
max_memory=10000):
BaseAgent.__init__(self,
env=env,
feature_transformer=feature_transformer,
gamma=gamma,
optimizer=optimizer)
self.model = policies.FCSoftmaxPolicy(self.n_dims,
self.n_actions,
n_hidden_layers=2,
n_hidden_channels=100,
nonlinearity=F.relu)
self.optimizer.setup(self.model)
#self.optimizer.add_hook(chainer.optimizer.GradientClipping(40))
self.replay_buffer = PrioritizedEpisodicReplayBuffer(
capacity=max_memory,
uniform_ratio=0.1,
default_priority_func=exp_return_of_episode,
wait_priority_after_sampling=False,
return_sample_weights=False)
self.agent = reinforce.REINFORCE(model=self.model,
optimizer=self.optimizer,
phi=phi,
batchsize=1,
act_deterministically=False)
def __init__(self, trial, width, height, action_size, lstm_size=128):
obs_size = width * height
self.head = MyHead(trial, width=width, height=height)
self.lstm = L.LSTM(self.head.n_output_channels, lstm_size)
self.pi = policies.FCSoftmaxPolicy(lstm_size, action_size)
self.v = v_function.FCVFunction(lstm_size)
super().__init__(self.head, self.lstm, self.pi, self.v)
def create_stochastic_policy_for_env(env):
assert isinstance(env.observation_space, gym.spaces.Box)
ndim_obs = env.observation_space.low.size
if isinstance(env.action_space, gym.spaces.Discrete):
return policies.FCSoftmaxPolicy(ndim_obs, env.action_space.n)
elif isinstance(env.action_space, gym.spaces.Box):
return policies.FCGaussianPolicy(ndim_obs,
env.action_space.low.size,
bound_mean=False)
else:
raise NotImplementedError()
def __init__(self,
env,
feature_transformer,
gamma=0.99,
optimizer='adam',
max_memory=10000):
BaseAgent.__init__(self,
env=env,
feature_transformer=feature_transformer,
gamma=gamma,
optimizer=optimizer)
self.model = agents.pcl.PCLSeparateModel(
pi=policies.FCSoftmaxPolicy(self.n_dims,
self.n_actions,
n_hidden_channels=100,
n_hidden_layers=2),
v=v_functions.FCVFunction(
self.n_dims,
n_hidden_channels=100,
n_hidden_layers=2,
),
)
self.optimizer.setup(self.model)
#self.optimizer.add_hook(chainer.optimizer.GradientClipping(40))
self.replay_buffer = \
chainerrl.replay_buffer.PrioritizedEpisodicReplayBuffer(
capacity=max_memory,
uniform_ratio=0.1,
default_priority_func=exp_return_of_episode,
wait_priority_after_sampling=False,
return_sample_weights=False)
self.agent = agents.pcl.PCL(model=self.model,
optimizer=self.optimizer,
replay_buffer=self.replay_buffer,
t_max=1,
gamma=self.gamma,
tau=1e-2,
phi=phi,
rollout_len=10,
batchsize=1,
disable_online_update=False,
n_times_replay=1,
replay_start_size=1000,
normalize_loss_by_steps=True,
act_deterministically=False,
backprop_future_values=False,
train_async=True)
def make_model(self, env):
n_hidden_channels = 20
n_dim_obs = env.observation_space.low.size
v = v_functions.FCVFunction(
n_dim_obs,
n_hidden_layers=1,
n_hidden_channels=n_hidden_channels,
nonlinearity=F.tanh,
last_wscale=0.01,
)
if self.discrete:
n_actions = env.action_space.n
pi = policies.FCSoftmaxPolicy(
n_dim_obs,
n_actions,
n_hidden_layers=1,
n_hidden_channels=n_hidden_channels,
nonlinearity=F.tanh,
last_wscale=0.01,
)
else:
n_dim_actions = env.action_space.low.size
pi = policies.FCGaussianPolicyWithStateIndependentCovariance(
n_dim_obs,
n_dim_actions,
n_hidden_layers=1,
n_hidden_channels=n_hidden_channels,
nonlinearity=F.tanh,
mean_wscale=0.01,
var_type='diagonal',
)
# Check if KL div supports double-backprop
fake_obs = np.zeros_like(env.observation_space.low, dtype=np.float32)
action_distrib = pi(fake_obs[None])
kl = action_distrib.kl(action_distrib)
old_style_funcs = trpo._find_old_style_function([kl])
if old_style_funcs:
self.skipTest("\
Chainer v{} does not support double backprop of these functions: {}.".format(
chainer.__version__, old_style_funcs))
return pi, v
def __init__(self, n_dims, n_actions):
self.head = links.Sequence(
L.ConvolutionND(ndim=1,
in_channels=n_dims,
out_channels=100,
ksize=3,
stride=1,
pad=1,
cover_all=True), F.relu)
self.pi = policies.FCSoftmaxPolicy(n_input_channels=100,
n_actions=n_actions,
n_hidden_layers=2,
n_hidden_channels=100)
self.v = v_functions.FCVFunction(n_input_channels=100,
n_hidden_layers=2,
n_hidden_channels=100)
super(A3CFF, self).__init__(self.head, self.pi, self.v)
def __init__(self,
ndim_obs,
n_discrete_entries,
n_hidden_layers=2,
n_hidden_channels=400,
beta=1.0):
self.pi = policies.FCSoftmaxPolicy(ndim_obs,
n_discrete_entries,
n_hidden_layers,
n_hidden_channels,
beta=beta)
self.v = chainerrl.v_functions.FCVFunction(
ndim_obs,
n_hidden_channels=n_hidden_channels,
n_hidden_layers=n_hidden_layers,
last_wscale=0.01,
)
super().__init__(self.pi, self.v)
def _test_abc(self,
t_max,
use_lstm,
discrete=True,
episodic=True,
steps=100000,
require_success=True):
nproc = 8
def make_env(process_idx, test):
size = 2
return ABC(size=size,
discrete=discrete,
episodic=episodic or test,
partially_observable=self.use_lstm,
deterministic=test)
sample_env = make_env(0, False)
action_space = sample_env.action_space
obs_space = sample_env.observation_space
def phi(x):
return x
n_hidden_channels = 20
n_hidden_layers = 2
nonlinearity = F.relu
if use_lstm:
if discrete:
model = a3c.A3CSharedModel(
shared=L.LSTM(obs_space.low.size, n_hidden_channels),
pi=policies.FCSoftmaxPolicy(
n_hidden_channels,
action_space.n,
n_hidden_channels=n_hidden_channels,
n_hidden_layers=n_hidden_layers,
nonlinearity=nonlinearity,
last_wscale=1e-2,
),
v=v_function.FCVFunction(
n_hidden_channels,
n_hidden_channels=n_hidden_channels,
n_hidden_layers=n_hidden_layers,
nonlinearity=nonlinearity,
last_wscale=1e-2,
),
)
else:
model = a3c.A3CSharedModel(
shared=L.LSTM(obs_space.low.size, n_hidden_channels),
pi=policies.FCGaussianPolicy(
n_hidden_channels,
action_space.low.size,
n_hidden_channels=n_hidden_channels,
n_hidden_layers=n_hidden_layers,
nonlinearity=nonlinearity,
var_wscale=1e-2,
var_bias=1,
bound_mean=True,
min_action=action_space.low,
max_action=action_space.high,
min_var=1e-1,
),
v=v_function.FCVFunction(
n_hidden_channels,
n_hidden_channels=n_hidden_channels,
n_hidden_layers=n_hidden_layers,
nonlinearity=nonlinearity,
last_wscale=1e-2,
),
)
else:
if discrete:
model = a3c.A3CSeparateModel(
pi=policies.FCSoftmaxPolicy(
obs_space.low.size,
action_space.n,
n_hidden_channels=n_hidden_channels,
n_hidden_layers=n_hidden_layers,
nonlinearity=nonlinearity,
last_wscale=1e-2,
),
v=v_function.FCVFunction(
obs_space.low.size,
n_hidden_channels=n_hidden_channels,
n_hidden_layers=n_hidden_layers,
nonlinearity=nonlinearity,
last_wscale=1e-2,
),
)
else:
model = a3c.A3CSeparateModel(
pi=policies.FCGaussianPolicy(
obs_space.low.size,
action_space.low.size,
n_hidden_channels=n_hidden_channels,
n_hidden_layers=n_hidden_layers,
nonlinearity=nonlinearity,
var_wscale=1e-2,
var_bias=1,
bound_mean=True,
min_action=action_space.low,
max_action=action_space.high,
min_var=1e-1,
),
v=v_function.FCVFunction(
obs_space.low.size,
n_hidden_channels=n_hidden_channels,
n_hidden_layers=n_hidden_layers,
nonlinearity=nonlinearity,
last_wscale=1e-2,
),
)
eps = 1e-8 if self.backprop_future_values else 1e-1
opt = rmsprop_async.RMSpropAsync(lr=5e-4, eps=eps, alpha=0.99)
opt.setup(model)
gamma = 0.5
tau = 1e-2
replay_buffer = chainerrl.replay_buffer.EpisodicReplayBuffer(10**5)
agent = pcl.PCL(model,
opt,
replay_buffer=replay_buffer,
t_max=t_max,
gamma=gamma,
tau=tau,
phi=phi,
n_times_replay=1,
batchsize=self.batchsize,
train_async=self.train_async,
backprop_future_values=self.backprop_future_values,
act_deterministically=True)
if self.train_async:
with warnings.catch_warnings(record=True) as warns:
chainerrl.experiments.train_agent_async(outdir=self.outdir,
processes=nproc,
make_env=make_env,
agent=agent,
steps=steps,
max_episode_len=2,
eval_interval=200,
eval_n_runs=5,
successful_score=1)
assert len(warns) == 0, warns[0]
# The agent returned by train_agent_async is not guaranteed to be
# successful because parameters could be modified by other
# processes after success. Thus here the successful model is loaded
# explicitly.
if require_success:
agent.load(os.path.join(self.outdir, 'successful'))
else:
agent.process_idx = 0
chainerrl.experiments.train_agent_with_evaluation(
agent=agent,
env=make_env(0, False),
eval_env=make_env(0, True),
outdir=self.outdir,
steps=steps,
max_episode_len=2,
eval_interval=200,
eval_n_runs=5,
successful_score=1)
agent.stop_episode()
# Test
env = make_env(0, True)
n_test_runs = 5
for _ in range(n_test_runs):
total_r = 0
obs = env.reset()
done = False
reward = 0.0
while not done:
action = agent.act(obs)
print('state:', obs, 'action:', action)
obs, reward, done, _ = env.step(action)
total_r += reward
if require_success:
self.assertAlmostEqual(total_r, 1)
agent.stop_episode()
def _test_abc(self,
t_max,
use_lstm,
discrete=True,
episodic=True,
steps=100000,
require_success=True):
nproc = 8
def make_env(process_idx, test):
size = 2
return ABC(size=size,
discrete=discrete,
episodic=episodic or test,
partially_observable=self.use_lstm,
deterministic=test)
sample_env = make_env(0, False)
action_space = sample_env.action_space
obs_space = sample_env.observation_space
def phi(x):
return x
n_hidden_channels = 20
n_hidden_layers = 1
nonlinearity = F.leaky_relu
replay_buffer = EpisodicReplayBuffer(10**4)
if use_lstm:
if discrete:
model = acer.ACERSharedModel(
shared=L.LSTM(obs_space.low.size, n_hidden_channels),
pi=policies.FCSoftmaxPolicy(
n_hidden_channels,
action_space.n,
n_hidden_channels=n_hidden_channels,
n_hidden_layers=n_hidden_layers,
nonlinearity=nonlinearity,
min_prob=1e-1),
q=q_function.FCStateQFunctionWithDiscreteAction(
n_hidden_channels,
action_space.n,
n_hidden_channels=n_hidden_channels,
n_hidden_layers=n_hidden_layers,
nonlinearity=nonlinearity),
)
else:
model = acer.ACERSDNSharedModel(
shared=L.LSTM(obs_space.low.size, n_hidden_channels),
pi=policies.FCGaussianPolicy(
n_hidden_channels,
action_space.low.size,
n_hidden_channels=n_hidden_channels,
n_hidden_layers=n_hidden_layers,
bound_mean=True,
min_action=action_space.low,
max_action=action_space.high,
nonlinearity=nonlinearity,
min_var=1e-1),
v=v_function.FCVFunction(
n_hidden_channels,
n_hidden_channels=n_hidden_channels,
n_hidden_layers=n_hidden_layers,
nonlinearity=nonlinearity),
adv=q_function.FCSAQFunction(
n_hidden_channels,
action_space.low.size,
n_hidden_channels=n_hidden_channels,
n_hidden_layers=n_hidden_layers,
nonlinearity=nonlinearity),
)
else:
if discrete:
model = acer.ACERSeparateModel(
pi=policies.FCSoftmaxPolicy(
obs_space.low.size,
action_space.n,
n_hidden_channels=n_hidden_channels,
n_hidden_layers=n_hidden_layers,
nonlinearity=nonlinearity,
min_prob=1e-1),
q=q_function.FCStateQFunctionWithDiscreteAction(
obs_space.low.size,
action_space.n,
n_hidden_channels=n_hidden_channels,
n_hidden_layers=n_hidden_layers,
nonlinearity=nonlinearity),
)
else:
model = acer.ACERSDNSeparateModel(
pi=policies.FCGaussianPolicy(
obs_space.low.size,
action_space.low.size,
n_hidden_channels=n_hidden_channels,
n_hidden_layers=n_hidden_layers,
bound_mean=True,
min_action=action_space.low,
max_action=action_space.high,
nonlinearity=nonlinearity,
min_var=1e-1),
v=v_function.FCVFunction(
obs_space.low.size,
n_hidden_channels=n_hidden_channels,
n_hidden_layers=n_hidden_layers,
nonlinearity=nonlinearity),
adv=q_function.FCSAQFunction(
obs_space.low.size,
action_space.low.size,
n_hidden_channels=n_hidden_channels,
n_hidden_layers=n_hidden_layers,
nonlinearity=nonlinearity),
)
eps = 1e-8
opt = rmsprop_async.RMSpropAsync(lr=1e-3, eps=eps, alpha=0.99)
opt.setup(model)
gamma = 0.5
beta = 1e-5
if self.n_times_replay == 0 and self.disable_online_update:
# At least one of them must be enabled
return
agent = acer.ACER(model,
opt,
replay_buffer=replay_buffer,
t_max=t_max,
gamma=gamma,
beta=beta,
phi=phi,
n_times_replay=self.n_times_replay,
act_deterministically=True,
disable_online_update=self.disable_online_update,
replay_start_size=100,
use_trust_region=self.use_trust_region)
max_episode_len = None if episodic else 2
with warnings.catch_warnings(record=True) as warns:
train_agent_async(outdir=self.outdir,
processes=nproc,
make_env=make_env,
agent=agent,
steps=steps,
max_episode_len=max_episode_len,
eval_interval=500,
eval_n_steps=None,
eval_n_episodes=5,
successful_score=1)
assert len(warns) == 0, warns[0]
# The agent returned by train_agent_async is not guaranteed to be
# successful because parameters could be modified by other processes
# after success. Thus here the successful model is loaded explicitly.
if require_success:
agent.load(os.path.join(self.outdir, 'successful'))
agent.stop_episode()
# Test
env = make_env(0, True)
n_test_runs = 5
for _ in range(n_test_runs):
total_r = 0
obs = env.reset()
done = False
reward = 0.0
while not done:
action = agent.act(obs)
print('state:', obs, 'action:', action)
obs, reward, done, _ = env.step(action)
total_r += reward
if require_success:
self.assertAlmostEqual(total_r, 1)
agent.stop_episode()
def _test_abc(self,
t_max,
use_lstm,
discrete=True,
episodic=True,
steps=100000,
require_success=True):
nproc = 8
def make_env(process_idx, test):
size = 2
return ABC(size=size,
discrete=discrete,
episodic=episodic or test,
partially_observable=self.use_lstm,
deterministic=test)
sample_env = make_env(0, False)
action_space = sample_env.action_space
obs_space = sample_env.observation_space
def phi(x):
return x
n_hidden_channels = 20
if use_lstm:
if discrete:
model = a3c.A3CSharedModel(
shared=L.LSTM(obs_space.low.size, n_hidden_channels),
pi=policies.FCSoftmaxPolicy(
n_hidden_channels,
action_space.n,
n_hidden_channels=n_hidden_channels,
n_hidden_layers=2,
nonlinearity=F.tanh,
last_wscale=1e-1,
),
v=v_function.FCVFunction(
n_hidden_channels,
n_hidden_channels=n_hidden_channels,
n_hidden_layers=2,
nonlinearity=F.tanh,
last_wscale=1e-1,
),
)
else:
model = a3c.A3CSharedModel(
shared=L.LSTM(obs_space.low.size, n_hidden_channels),
pi=policies.FCGaussianPolicy(
n_hidden_channels,
action_space.low.size,
n_hidden_channels=n_hidden_channels,
n_hidden_layers=2,
nonlinearity=F.tanh,
mean_wscale=1e-1,
),
v=v_function.FCVFunction(
n_hidden_channels,
n_hidden_channels=n_hidden_channels,
n_hidden_layers=2,
nonlinearity=F.tanh,
last_wscale=1e-1,
),
)
else:
if discrete:
model = a3c.A3CSeparateModel(
pi=policies.FCSoftmaxPolicy(
obs_space.low.size,
action_space.n,
n_hidden_channels=n_hidden_channels,
n_hidden_layers=2,
nonlinearity=F.tanh,
last_wscale=1e-1,
),
v=v_function.FCVFunction(
obs_space.low.size,
n_hidden_channels=n_hidden_channels,
n_hidden_layers=2,
nonlinearity=F.tanh,
last_wscale=1e-1,
),
)
else:
model = a3c.A3CSeparateModel(
pi=policies.FCGaussianPolicy(
obs_space.low.size,
action_space.low.size,
n_hidden_channels=n_hidden_channels,
n_hidden_layers=2,
nonlinearity=F.tanh,
mean_wscale=1e-1,
),
v=v_function.FCVFunction(
obs_space.low.size,
n_hidden_channels=n_hidden_channels,
n_hidden_layers=2,
nonlinearity=F.tanh,
last_wscale=1e-1,
),
)
opt = chainer.optimizers.Adam()
opt.setup(model)
opt.add_hook(chainer.optimizer.GradientClipping(1))
gamma = 0.8
beta = 1e-2
agent = a3c.A3C(model,
opt,
t_max=t_max,
gamma=gamma,
beta=beta,
phi=phi,
act_deterministically=True)
max_episode_len = None if episodic else 2
with warnings.catch_warnings(record=True) as warns:
train_agent_async(outdir=self.outdir,
processes=nproc,
make_env=make_env,
agent=agent,
steps=steps,
max_episode_len=max_episode_len,
eval_interval=500,
eval_n_steps=None,
eval_n_episodes=5,
successful_score=1)
assert len(warns) == 0, warns[0]
# The agent returned by train_agent_async is not guaranteed to be
# successful because parameters could be modified by other processes
# after success. Thus here the successful model is loaded explicitly.
if require_success:
agent.load(os.path.join(self.outdir, 'successful'))
agent.stop_episode()
# Test
env = make_env(0, True)
n_test_runs = 5
for _ in range(n_test_runs):
total_r = 0
obs = env.reset()
done = False
reward = 0.0
while not done:
action = agent.act(obs)
print('state:', obs, 'action:', action)
obs, reward, done, _ = env.step(action)
total_r += reward
if require_success:
self.assertAlmostEqual(total_r, 1)
agent.stop_episode()
def __init__(self, n_actions):
self.head = links.NIPSDQNHead()
self.pi = policies.FCSoftmaxPolicy(self.head.n_output_channels,
n_actions)
self.v = v_functions.FCVFunction(self.head.n_output_channels)
super().__init__(self.head, self.pi, self.v)
def _test_abc(self,
use_lstm,
discrete=True,
steps=1000000,
require_success=True,
gpu=-1):
def make_env(process_idx, test):
size = 2
return ABC(size=size,
discrete=discrete,
episodic=True,
partially_observable=self.use_lstm,
deterministic=test)
sample_env = make_env(0, False)
action_space = sample_env.action_space
obs_space = sample_env.observation_space
def phi(x):
return x
n_hidden_channels = 20
n_hidden_layers = 1
nonlinearity = F.leaky_relu
if use_lstm:
if discrete:
model = chainerrl.links.Sequence(
L.LSTM(obs_space.low.size,
n_hidden_channels,
forget_bias_init=1),
policies.FCSoftmaxPolicy(
n_hidden_channels,
action_space.n,
n_hidden_channels=n_hidden_channels,
n_hidden_layers=n_hidden_layers,
nonlinearity=nonlinearity),
)
else:
model = chainerrl.links.Sequence(
L.LSTM(obs_space.low.size,
n_hidden_channels,
forget_bias_init=1),
policies.FCGaussianPolicy(
n_hidden_channels,
action_space.low.size,
n_hidden_channels=n_hidden_channels,
n_hidden_layers=n_hidden_layers,
bound_mean=True,
min_action=action_space.low,
max_action=action_space.high,
nonlinearity=nonlinearity,
))
else:
if discrete:
model = policies.FCSoftmaxPolicy(
obs_space.low.size,
action_space.n,
n_hidden_channels=n_hidden_channels,
n_hidden_layers=n_hidden_layers,
nonlinearity=nonlinearity)
else:
model = policies.FCGaussianPolicy(
obs_space.low.size,
action_space.low.size,
n_hidden_channels=n_hidden_channels,
n_hidden_layers=n_hidden_layers,
bound_mean=True,
min_action=action_space.low,
max_action=action_space.high,
nonlinearity=nonlinearity,
)
if gpu >= 0:
chainer.cuda.get_device_from_id(gpu).use()
model.to_gpu()
opt = optimizers.Adam()
opt.setup(model)
beta = 1e-2
agent = chainerrl.agents.REINFORCE(
model,
opt,
beta=beta,
phi=phi,
batchsize=self.batchsize,
backward_separately=self.backward_separately,
act_deterministically=True,
)
chainerrl.experiments.train_agent_with_evaluation(
agent=agent,
env=make_env(0, False),
eval_env=make_env(0, True),
outdir=self.outdir,
steps=steps,
train_max_episode_len=2,
eval_interval=500,
eval_n_steps=None,
eval_n_episodes=5,
successful_score=1)
# Test
env = make_env(0, True)
n_test_runs = 5
for _ in range(n_test_runs):
total_r = 0
obs = env.reset()
done = False
reward = 0.0
while not done:
action = agent.act(obs)
print('state:', obs, 'action:', action)
obs, reward, done, _ = env.step(action)
total_r += reward
if require_success:
self.assertAlmostEqual(total_r, 1)
agent.stop_episode()
def _test_abc(self,
t_max,
use_lstm,
discrete=True,
episodic=True,
steps=1000000):
nproc = 8
def make_env(process_idx, test):
size = 2
return ABC(size=size,
discrete=discrete,
episodic=episodic or test,
partially_observable=self.use_lstm,
deterministic=test)
sample_env = make_env(0, False)
action_space = sample_env.action_space
obs_space = sample_env.observation_space
def phi(x):
return x
n_hidden_channels = 20
if use_lstm:
if discrete:
model = a3c.A3CSharedModel(
shared=L.LSTM(obs_space.low.size, n_hidden_channels),
pi=policies.FCSoftmaxPolicy(
n_hidden_channels,
action_space.n,
n_hidden_channels=n_hidden_channels,
n_hidden_layers=2),
v=v_function.FCVFunction(
n_hidden_channels,
n_hidden_channels=n_hidden_channels,
n_hidden_layers=2),
)
else:
model = a3c.A3CSharedModel(
shared=L.LSTM(obs_space.low.size, n_hidden_channels),
pi=policies.FCGaussianPolicy(
n_hidden_channels,
action_space.low.size,
n_hidden_channels=n_hidden_channels,
n_hidden_layers=2,
bound_mean=True,
min_action=action_space.low,
max_action=action_space.high),
v=v_function.FCVFunction(
n_hidden_channels,
n_hidden_channels=n_hidden_channels,
n_hidden_layers=2),
)
else:
if discrete:
model = a3c.A3CSeparateModel(
pi=policies.FCSoftmaxPolicy(
obs_space.low.size,
action_space.n,
n_hidden_channels=n_hidden_channels,
n_hidden_layers=2),
v=v_function.FCVFunction(
obs_space.low.size,
n_hidden_channels=n_hidden_channels,
n_hidden_layers=2),
)
else:
model = a3c.A3CSeparateModel(
pi=policies.FCGaussianPolicy(
obs_space.low.size,
action_space.low.size,
n_hidden_channels=n_hidden_channels,
n_hidden_layers=2,
bound_mean=True,
min_action=action_space.low,
max_action=action_space.high),
v=v_function.FCVFunction(
obs_space.low.size,
n_hidden_channels=n_hidden_channels,
n_hidden_layers=2),
)
eps = 1e-1 if discrete else 1e-2
opt = rmsprop_async.RMSpropAsync(lr=5e-4, eps=eps, alpha=0.99)
opt.setup(model)
gamma = 0.9
beta = 1e-2
agent = a3c.A3C(model,
opt,
t_max=t_max,
gamma=gamma,
beta=beta,
phi=phi,
act_deterministically=True)
max_episode_len = None if episodic else 2
train_agent_async(outdir=self.outdir,
processes=nproc,
make_env=make_env,
agent=agent,
steps=steps,
max_episode_len=max_episode_len,
eval_interval=500,
eval_n_runs=5,
successful_score=1)
# The agent returned by train_agent_async is not guaranteed to be
# successful because parameters could be modified by other processes
# after success. Thus here the successful model is loaded explicitly.
agent.load(os.path.join(self.outdir, 'successful'))
agent.stop_episode()
# Test
env = make_env(0, True)
n_test_runs = 5
for _ in range(n_test_runs):
total_r = 0
obs = env.reset()
done = False
reward = 0.0
while not done:
action = agent.act(obs)
print('state:', obs, 'action:', action)
obs, reward, done, _ = env.step(action)
total_r += reward
self.assertAlmostEqual(total_r, 1)
agent.stop_episode()
