class Agent:
def __init__(self,
actor_dims,
critic_dims,
n_actions,
n_agents,
agent_idx,
chkpt_dir,
alpha=0.01,
beta=0.01,
fc1=64,
fc2=64,
gamma=0.95,
tau=0.01):
"""
Args:
actor_dims:
critic_dims:
n_actions: number of actions
n_agents:
agent_idx: agent index
chkpt_dir: checkpoint directory
alpha: learning rate
beta: learning rate
fc1:
fc2:
gamma: discount factor
tau: soft update parameter
"""
self.gamma = gamma
self.tau = tau
self.n_actions = n_actions
self.agent_name = 'agent_%s' % agent_idx
# e.g., name = agent_1_actor
self.actor = ActorNetwork(alpha,
actor_dims,
fc1,
fc2,
n_actions,
chkpt_dir=chkpt_dir,
name=self.agent_name + '_actor')
self.critic = CriticNetwork(beta,
critic_dims,
fc1,
fc2,
n_agents,
n_actions,
chkpt_dir=chkpt_dir,
name=self.agent_name + '_critic')
self.target_actor = ActorNetwork(alpha,
actor_dims,
fc1,
fc2,
n_actions,
chkpt_dir=chkpt_dir,
name=self.agent_name +
'_target_actor')
self.target_critic = CriticNetwork(beta,
critic_dims,
fc1,
fc2,
n_agents,
n_actions,
chkpt_dir=chkpt_dir,
name=self.agent_name +
'_target_critic')
# initially target networks and networks have the same parameters
self.update_network_parameters(tau=1)
def choose_action(self, observation):
"""
Args:
observation:
Returns: action w.r.t. the current policy and exploration
"""
state = T.tensor([observation], dtype=T.float).to(self.actor.device)
# action of current policy
actions = self.actor.forward(state)
# exploration (0.1 is the parameter of the exploration)
noise = 0.1 * T.rand(self.n_actions).to(self.actor.device)
# print(f"action={action}, noise={noise}")
action = actions + noise
# action = actions
return action.detach().cpu().numpy()[0]
def update_network_parameters(self, tau=None):
# use default tau if nothing is input
if tau is None:
tau = self.tau
target_actor_params = self.target_actor.named_parameters()
actor_params = self.actor.named_parameters()
# soft update of target networks
target_actor_state_dict = dict(target_actor_params)
actor_state_dict = dict(actor_params)
for name in actor_state_dict:
actor_state_dict[name] = tau * actor_state_dict[name].clone() + \
(1 - tau) * target_actor_state_dict[
name].clone()
self.target_actor.load_state_dict(actor_state_dict)
target_critic_params = self.target_critic.named_parameters()
critic_params = self.critic.named_parameters()
target_critic_state_dict = dict(target_critic_params)
critic_state_dict = dict(critic_params)
for name in critic_state_dict:
critic_state_dict[name] = tau * critic_state_dict[name].clone() + \
(1 - tau) * target_critic_state_dict[
name].clone()
self.target_critic.load_state_dict(critic_state_dict)
def save_models(self):
self.actor.save_checkpoint()
self.target_actor.save_checkpoint()
self.critic.save_checkpoint()
self.target_critic.save_checkpoint()
def load_models(self):
self.actor.load_checkpoint()
self.target_actor.load_checkpoint()
self.critic.load_checkpoint()
self.target_critic.load_checkpoint()
class Agent():
def __init__(self,
alpha,
beta,
input_dims,
tau,
n_actions,
gamma=0.99,
max_size=50000,
fc1_dims=400,
fc2_dims=300,
batch_size=32):
self.gamma = gamma
self.tau = tau
self.batch_size = batch_size
self.alpha = alpha
self.beta = beta
self.memory = ReplayBuffer(max_size, input_dims, n_actions)
self.noise = OUActionNoise(mu=np.zeros(n_actions))
self.actor = ActorNetwork(alpha,
input_dims,
fc1_dims,
fc2_dims,
n_actions=n_actions,
name='actor')
self.critic = CriticNetwork(beta,
input_dims,
fc1_dims,
fc2_dims,
n_actions=n_actions,
name='critic')
self.target_actor = ActorNetwork(alpha,
input_dims,
fc1_dims,
fc2_dims,
n_actions=n_actions,
name='target_actor')
self.target_critic = CriticNetwork(beta,
input_dims,
fc1_dims,
fc2_dims,
n_actions=n_actions,
name='target_critic')
self.update_network_parameters(
tau=1) # for the first time target_actor and actor are same
def choose_action(self, observation):
self.actor.eval(
) # we are setting our actor network to eval mode because we have batch normalization layer
# and we dont want to calculate statistics for that layer at this step
state = T.tensor([observation], dtype=T.float).to(self.actor.device)
mu = self.actor.forward(state).to(self.actor.device)
mu_prime = mu + T.tensor(self.noise(), dtype=T.float).to(
self.actor.device)
self.actor.train()
return mu_prime.cpu().detach().numpy()[0]
def remember(self, state, action, reward, state_, done):
self.memory.store_transition(state, action, reward, state_, done)
def save_models(self):
self.actor.save_checkpoint()
self.target_actor.save_checkpoint()
self.critic.save_checkpoint()
self.target_critic.save_checkpoint()
def load_models(self):
self.actor.load_checkpoint()
self.target_actor.load_checkpoint()
self.critic.load_checkpoint()
self.target_critic.load_checkpoint()
def learn(self):
if self.memory.mem_cntr < self.batch_size:
return
states, actions, rewards, states_, done = self.memory.sample_buffer(
self.batch_size)
states = T.tensor(states, dtype=T.float).to(self.actor.device)
states_ = T.tensor(states_, dtype=T.float).to(self.actor.device)
actions = T.tensor(actions, dtype=T.float).to(self.actor.device)
rewards = T.tensor(rewards, dtype=T.float).to(self.actor.device)
done = T.tensor(done).to(self.actor.device)
target_actions = self.target_actor.forward(states_)
critic_value_ = self.target_critic.forward(states_, target_actions)
critic_value = self.critic.forward(states, actions)
critic_value_[done] = 0.0
critic_value_ = critic_value_.view(-1)
target = rewards + self.gamma * critic_value_
target = target.view(self.batch_size, 1)
self.critic.optimizer.zero_grad()
critic_loss = F.mse_loss(target, critic_value)
critic_loss.backward()
self.critic.optimizer.step()
self.actor.optimizer.zero_grad()
actor_loss = -self.critic.forward(states, self.actor.forward(states))
actor_loss = T.mean(actor_loss)
actor_loss.backward()
self.actor.optimizer.step()
self.update_network_parameters(
) # sending tau None so that local tau variable there takes the value of class tau variable
def update_network_parameters(self, tau=None):
if tau is None:
tau = self.tau
actor_params = self.actor.named_parameters()
critic_params = self.critic.named_parameters()
target_actor_params = self.target_actor.named_parameters()
target_critic_params = self.target_critic.named_parameters()
critic_state_dict = dict(critic_params)
actor_state_dict = dict(actor_params)
target_critic_state_dict = dict(target_critic_params)
target_actor_state_dict = dict(target_actor_params)
for name in critic_state_dict:
critic_state_dict[name] = tau * critic_state_dict[name].clone() + (
1 - tau) * target_critic_state_dict[name].clone()
for name in actor_state_dict:
actor_state_dict[name] = tau * actor_state_dict[name].clone() + (
1 - tau) * target_actor_state_dict[name].clone()
self.target_critic.load_state_dict(critic_state_dict)
self.target_actor.load_state_dict(actor_state_dict)
def main(): args = parse_arguments() env = gym.make(args.env_name) actor = ActorNetwork(env.observation_space.shape[0],env.action_space.n) actor.load_state_dict(torch.load(args.load_dir)) test(env,actor,args.render)
class Agent:
def __init__(self,
actor_dims,
critic_dims,
n_actions,
n_agents,
agent_idx,
chkpt_dir,
alpha=0.01,
beta=0.01,
fc1=64,
fc2=64,
gamma=0.95,
tau=0.01):
self.gamma = gamma
self.tau = tau
self.n_actions = n_actions
self.agent_name = 'agent_%s' % agent_idx
self.actor = ActorNetwork(alpha,
actor_dims,
fc1,
fc2,
n_actions,
chkpt_dir=chkpt_dir,
name=self.agent_name + '_actor')
self.critic = CriticNetwork(beta,
critic_dims,
fc1,
fc2,
n_agents,
n_actions,
chkpt_dir=chkpt_dir,
name=self.agent_name + '_critic')
self.target_actor = ActorNetwork(alpha,
actor_dims,
fc1,
fc2,
n_actions,
chkpt_dir=chkpt_dir,
name=self.agent_name +
'_target_actor')
self.target_critic = CriticNetwork(beta,
critic_dims,
fc1,
fc2,
n_agents,
n_actions,
chkpt_dir=chkpt_dir,
name=self.agent_name +
'_target_critic')
self.update_network_parameters(tau=1)
def choose_action(self, observation):
state = T.tensor([observation], dtype=T.float).to(self.actor.device)
actions = self.actor.forward(state)
noise = T.rand(self.n_actions).to(self.actor.device)
action = actions + noise
return action.detach().cpu().numpy()[0]
def update_network_parameters(self, tau=None):
if tau is None:
tau = self.tau
target_actor_params = self.target_actor.named_parameters()
actor_params = self.actor.named_parameters()
target_actor_state_dict = dict(target_actor_params)
actor_state_dict = dict(actor_params)
for name in actor_state_dict:
actor_state_dict[name] = tau*actor_state_dict[name].clone() + \
(1-tau)*target_actor_state_dict[name].clone()
self.target_actor.load_state_dict(actor_state_dict)
target_critic_params = self.target_critic.named_parameters()
critic_params = self.critic.named_parameters()
target_critic_state_dict = dict(target_critic_params)
critic_state_dict = dict(critic_params)
for name in critic_state_dict:
critic_state_dict[name] = tau*critic_state_dict[name].clone() + \
(1-tau)*target_critic_state_dict[name].clone()
self.target_critic.load_state_dict(critic_state_dict)
def save_models(self):
self.actor.save_checkpoint()
self.target_actor.save_checkpoint()
self.critic.save_checkpoint()
self.target_critic.save_checkpoint()
def load_models(self):
self.actor.load_checkpoint()
self.target_actor.load_checkpoint()
self.critic.load_checkpoint()
self.target_critic.load_checkpoint()
class Agent(object):
def __init__(self,
alpha,
beta,
input_dims,
action_bound,
tau,
env,
gamma=0.99,
n_actions=2,
max_size=1000000,
layer1_size=400,
layer2_size=300,
batch_size=64):
self.gamma = gamma
self.tau = tau
self.memory = ReplayBuffer(max_size, input_dims, n_actions)
self.batch_size = batch_size
self.action_bound = action_bound
self.actor = ActorNetwork(alpha,
input_dims,
layer1_size,
layer2_size,
n_actions=n_actions,
name='Actor')
self.critic = CriticNetwork(beta,
input_dims,
layer1_size,
layer2_size,
n_actions=n_actions,
name='Critic')
self.target_actor = ActorNetwork(alpha,
input_dims,
layer1_size,
layer2_size,
n_actions=n_actions,
name='TargetActor')
self.target_critic = CriticNetwork(beta,
input_dims,
layer1_size,
layer2_size,
n_actions=n_actions,
name='TargetCritic')
self.noise = OUActionNoise(mu=np.zeros(n_actions))
self.update_network_parameters(tau=1)
def choose_action(self, observation):
self.actor.eval()
observation = T.tensor(observation,
dtype=T.float).to(self.actor.device)
mu = self.actor.forward(observation).to(self.actor.device)
mu_prime = mu + T.tensor(self.noise(), dtype=T.float).to(
self.actor.device)
self.actor.train()
return (mu_prime * T.tensor(self.action_bound)).cpu().detach().numpy()
def remember(self, state, action, reward, new_state, done):
self.memory.store_transition(state, action, reward, new_state, done)
def learn(self):
if self.memory.mem_cntr < self.batch_size:
return
state, action, reward, new_state, done = \
self.memory.sample_buffer(self.batch_size)
reward = T.tensor(reward, dtype=T.float).to(self.critic.device)
done = T.tensor(done).to(self.critic.device)
new_state = T.tensor(new_state, dtype=T.float).to(self.critic.device)
action = T.tensor(action, dtype=T.float).to(self.critic.device)
state = T.tensor(state, dtype=T.float).to(self.critic.device)
self.target_actor.eval()
self.target_critic.eval()
self.critic.eval()
target_actions = self.target_actor.forward(new_state)
critic_value_ = self.target_critic.forward(new_state, target_actions)
critic_value = self.critic.forward(state, action)
target = []
for j in range(self.batch_size):
target.append(reward[j] + self.gamma * critic_value_[j] * done[j])
target = T.tensor(target).to(self.critic.device)
target = target.view(self.batch_size, 1)
self.critic.train()
self.critic.optimizer.zero_grad()
critic_loss = F.mse_loss(target, critic_value)
critic_loss.backward()
self.critic.optimizer.step()
self.critic.eval()
self.actor.optimizer.zero_grad()
mu = self.actor.forward(state)
self.actor.train()
actor_loss = -self.critic.forward(state, mu)
actor_loss = T.mean(actor_loss)
actor_loss.backward()
self.actor.optimizer.step()
self.update_network_parameters()
def update_network_parameters(self, tau=None):
if tau is None:
tau = self.tau
actor_params = self.actor.named_parameters()
critic_params = self.critic.named_parameters()
target_actor_params = self.target_actor.named_parameters()
target_critic_params = self.target_critic.named_parameters()
critic_state_dict = dict(critic_params)
actor_state_dict = dict(actor_params)
target_critic_dict = dict(target_critic_params)
target_actor_dict = dict(target_actor_params)
for name in critic_state_dict:
critic_state_dict[name] = tau*critic_state_dict[name].clone() + \
(1-tau)*target_critic_dict[name].clone()
self.target_critic.load_state_dict(critic_state_dict)
for name in actor_state_dict:
actor_state_dict[name] = tau*actor_state_dict[name].clone() + \
(1-tau)*target_actor_dict[name].clone()
self.target_actor.load_state_dict(actor_state_dict)
"""
#Verify that the copy assignment worked correctly
target_actor_params = self.target_actor.named_parameters()
target_critic_params = self.target_critic.named_parameters()
critic_state_dict = dict(target_critic_params)
actor_state_dict = dict(target_actor_params)
print('\nActor Networks', tau)
for name, param in self.actor.named_parameters():
print(name, T.equal(param, actor_state_dict[name]))
print('\nCritic Networks', tau)
for name, param in self.critic.named_parameters():
print(name, T.equal(param, critic_state_dict[name]))
input()
"""
def save_models(self):
self.actor.save_checkpoint()
self.target_actor.save_checkpoint()
self.critic.save_checkpoint()
self.target_critic.save_checkpoint()
def load_models(self):
self.actor.load_checkpoint()
self.target_actor.load_checkpoint()
self.critic.load_checkpoint()
self.target_critic.load_checkpoint()
def check_actor_params(self):
current_actor_params = self.actor.named_parameters()
current_actor_dict = dict(current_actor_params)
original_actor_dict = dict(self.original_actor.named_parameters())
original_critic_dict = dict(self.original_critic.named_parameters())
current_critic_params = self.critic.named_parameters()
current_critic_dict = dict(current_critic_params)
print('Checking Actor parameters')
for param in current_actor_dict:
print(
param,
T.equal(original_actor_dict[param], current_actor_dict[param]))
print('Checking critic parameters')
for param in current_critic_dict:
print(
param,
T.equal(original_critic_dict[param],
current_critic_dict[param]))
input()
class Agent():
def __init__(self,
alpha,
beta,
input_dims,
tau,
env,
action_bound,
gamma=0.99,
update_actor_interval=2,
warmup=1000,
n_actions=2,
max_size=1000000,
layer1_size=400,
layer2_size=300,
batch_size=100,
noise=0.1):
self.gamma = gamma
self.tau = tau
self.max_action = env.action_space.high
self.min_action = env.action_space.low
self.memory = ReplayBuffer(max_size, input_dims, n_actions)
self.batch_size = batch_size
self.learn_step_cntr = 0
self.time_step = 0
self.warmup = warmup
self.n_actions = n_actions
self.update_actor_iter = update_actor_interval
self.action_bound = action_bound
self.actor = ActorNetwork(alpha,
input_dims,
layer1_size,
layer2_size,
n_actions=n_actions,
name='actor')
self.critic_1 = CriticNetwork(beta,
input_dims,
layer1_size,
layer2_size,
n_actions=n_actions,
name='critic_1')
self.critic_2 = CriticNetwork(beta,
input_dims,
layer1_size,
layer2_size,
n_actions=n_actions,
name='critic_2')
self.target_actor = ActorNetwork(alpha,
input_dims,
layer1_size,
layer2_size,
n_actions=n_actions,
name='target_actor')
self.target_critic_1 = CriticNetwork(beta,
input_dims,
layer1_size,
layer2_size,
n_actions=n_actions,
name='target_critic_1')
self.target_critic_2 = CriticNetwork(beta,
input_dims,
layer1_size,
layer2_size,
n_actions=n_actions,
name='target_critic_2')
self.noise = noise
self.update_network_parameters(tau=1)
def choose_action(self, observation):
if self.time_step < self.warmup:
mu = T.tensor(
np.random.normal(scale=self.noise, size=(self.n_actions, )))
else:
state = T.tensor(observation, dtype=T.float).to(self.actor.device)
mu = self.actor.forward(state).to(self.actor.device)
mu_prime = mu + T.tensor(np.random.normal(scale=self.noise),
dtype=T.float).to(self.actor.device)
mu_prime = T.clamp(mu_prime, self.min_action[0], self.max_action[0])
self.time_step += 1
return (mu_prime * T.tensor(self.action_bound)).cpu().detach().numpy()
def remember(self, state, action, reward, new_state, done):
self.memory.store_transition(state, action, reward, new_state, done)
def learn(self):
if self.memory.mem_cntr < self.batch_size:
return
state, action, reward, new_state, done = \
self.memory.sample_buffer(self.batch_size)
reward = T.tensor(reward, dtype=T.float).to(self.critic_1.device)
done = T.tensor(done).to(self.critic_1.device)
state_ = T.tensor(new_state, dtype=T.float).to(self.critic_1.device)
state = T.tensor(state, dtype=T.float).to(self.critic_1.device)
action = T.tensor(action, dtype=T.float).to(self.critic_1.device)
target_actions = self.target_actor.forward(state_)
target_actions = target_actions + \
T.clamp(T.tensor(np.random.normal(scale=0.2)), -0.5, 0.5)
target_actions = T.clamp(target_actions, self.min_action[0],
self.max_action[0])
q1_ = self.target_critic_1.forward(state_, target_actions)
q2_ = self.target_critic_2.forward(state_, target_actions)
q1 = self.critic_1.forward(state, action)
q2 = self.critic_2.forward(state, action)
q1_[done] = 0.0
q2_[done] = 0.0
q1_ = q1_.view(-1)
q2_ = q2_.view(-1)
critic_value_ = T.min(q1_, q2_)
target = reward + self.gamma * critic_value_
target = target.view(self.batch_size, 1)
self.critic_1.optimizer.zero_grad()
self.critic_2.optimizer.zero_grad()
q1_loss = F.mse_loss(target, q1)
q2_loss = F.mse_loss(target, q2)
critic_loss = q1_loss + q2_loss
critic_loss.backward()
self.critic_1.optimizer.step()
self.critic_2.optimizer.step()
self.learn_step_cntr += 1
if self.learn_step_cntr % self.update_actor_iter != 0:
return
self.actor.optimizer.zero_grad()
actor_q1_loss = self.critic_1.forward(state, self.actor.forward(state))
actor_loss = -T.mean(actor_q1_loss)
actor_loss.backward()
self.actor.optimizer.step()
self.update_network_parameters()
def update_network_parameters(self, tau=None):
if tau is None:
tau = self.tau
actor_params = self.actor.named_parameters()
critic_1_params = self.critic_1.named_parameters()
critic_2_params = self.critic_2.named_parameters()
target_actor_params = self.target_actor.named_parameters()
target_critic_1_params = self.target_critic_1.named_parameters()
target_critic_2_params = self.target_critic_2.named_parameters()
critic_1 = dict(critic_1_params)
critic_2 = dict(critic_2_params)
actor = dict(actor_params)
target_actor = dict(target_actor_params)
target_critic_1 = dict(target_critic_1_params)
target_critic_2 = dict(target_critic_2_params)
for name in critic_1:
critic_1[name] = tau*critic_1[name].clone() + \
(1-tau)*target_critic_1[name].clone()
for name in critic_2:
critic_2[name] = tau*critic_2[name].clone() + \
(1-tau)*target_critic_2[name].clone()
for name in actor:
actor[name] = tau*actor[name].clone() + \
(1-tau)*target_actor[name].clone()
self.target_critic_1.load_state_dict(critic_1)
self.target_critic_2.load_state_dict(critic_2)
self.target_actor.load_state_dict(actor)
def save_models(self):
self.actor.save_checkpoint()
self.target_actor.save_checkpoint()
self.critic_1.save_checkpoint()
self.critic_2.save_checkpoint()
self.target_critic_1.save_checkpoint()
self.target_critic_2.save_checkpoint()
def load_models(self):
self.actor.load_checkpoint()
self.target_actor.load_checkpoint()
self.critic_1.load_checkpoint()
self.critic_2.load_checkpoint()
self.target_critic_1.load_checkpoint()
self.target_critic_2.load_checkpoint()
def main(args):
args = parse_arguments()
args.cuda = not args.no_cuda and torch.cuda.is_available()
env = gym.make(args.env_name)
os.environ['OMP_NUM_THREADS'] = '1'
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed_all(args.seed)
torch.set_num_threads(1)
writer = SummaryWriter(log_dir=args.save_dir)
actor = ActorNetwork(env.observation_space.shape[0], env.action_space.n)
critic = CriticNetwork(env.observation_space.shape[0])
if args.continue_training:
try:
actorState = torch.load(args.load_dir,
map_location=lambda storage, loc: storage)
actor.load_state_dict(actorState)
except:
assert False, "Unable to find a model to load"
if args.cuda:
actor.cuda()
critic.cuda()
actor_optimizer = optim.Adam(actor.parameters(), lr=args.lr)
critic_optimizer = optim.Adam(critic.parameters(), lr=args.lr)
N = args.nsteps
eps = 1.0
obsarr = []
rewardarr = []
actionlossarr = []
actionarr = []
valuearr = []
ep_len = 0
for ep in range(args.num_episodes):
done = False
obs = env.reset()
while not done:
ep_len += 1
obs_var = Variable(torch.from_numpy(obs).float(), volatile=True)
action = actor.get_action(obs_var)
value = critic(obs_var)
action = action.data[0]
next_obs, reward, done, _ = env.step(action)
if args.render:
env.render()
obsarr.append(obs)
actionarr.append(action)
rewardarr.append(reward)
valuearr.append(value)
obs = next_obs
T = len(obsarr)
G = [0] * T
batch_obs = Variable(torch.from_numpy(np.stack(obsarr)).float())
batch_act = Variable(torch.from_numpy(np.array(actionarr)))
logprobvar = actor.evaluate_actions(batch_obs, batch_act)
valvar = critic(batch_obs)
logprobvar = logprobvar.squeeze(1)
valvar = valvar.squeeze(1)
for t in reversed(range(T)):
V = 0
if t + N < T:
V = valvar[t + N].data[0]
G[t] = pow(args.gamma, N) * V
u = min(N, T - t)
for k in range(u):
G[t] += pow(args.gamma, k) * rewardarr[t + k]
Gtensor = Variable(torch.FloatTensor(G))
adv = 0.01 * Gtensor - valvar.detach()
action_loss = -(adv * logprobvar).mean()
value_loss = (0.01 * Gtensor - valvar).pow(2).mean()
actionlossarr.append(action_loss)
critic_optimizer.zero_grad()
value_loss.backward()
torch.nn.utils.clip_grad_norm(critic.parameters(), 3)
critic_optimizer.step()
if ep % args.update_freq == 0:
actor_optimizer.zero_grad()
l = torch.cat(actionlossarr).mean()
l.backward()
torch.nn.utils.clip_grad_norm(actor.parameters(), 3)
actor_optimizer.step()
r = np.array(rewardarr).sum() / args.update_freq
print("Episode: {} | Reward: {:.3f}| Length: {}".format(
ep, r, ep_len / args.update_freq))
obsarr = []
rewardarr = []
actionlossarr = []
actionarr = []
ep_len = 0
if ep % 500 == 0:
torch.save(actor.state_dict(),
args.save_dir + '/' + args.env_name + '.pt')
rm, rs, em = test(env, actor, False)
writer.add_scalar('test/reward_mean', rm, ep)
writer.add_scalar('test/reward_std', rs, ep)
writer.add_scalar('test/ep_len_mean', em, ep)
writer.export_scalars_to_json(args.save_dir + '/' + args.env_name +
'_scalars.json')
writer.add_scalar('train/reward', r, ep)