def evaluate(net, save_domains=False, baseline=None):
test_env = SubprocVecEnv([
lambda: gym.make('SysAdmin-v0', save_domain=save_domains)
for i in range(config.eval_batch)
],
in_series=(config.eval_batch // config.cpus),
context='fork')
tqdm_val = tqdm(desc='Validating',
total=config.eval_problems,
unit=' problems')
with torch.no_grad():
net.eval()
r_tot = 0.
problems_finished = 0.
rewards = []
steps = 0
s = test_env.reset()
while problems_finished < config.eval_problems:
steps += 1
if not baseline:
a, v, pi, pi_full = net(s)
else:
a = random_action(s, baseline, config.multi)
s, r, d, i = test_env.step(a)
r_tot += np.sum(r)
problems_finished += np.sum(d)
rewards += [x['reward_total'] for x in itertools.compress(i, d)]
tqdm_val.update(np.sum(d))
r_avg_ps = r_tot / (steps * config.eval_batch
) # average reward per step
r_avg_pp = r_tot / problems_finished # average reward per problem
net.train()
if args.print_raw:
rew_mean = np.mean(rewards)
rew_ci95 = 1.96 * scipy.stats.sem(rewards)
print(f"{rew_mean:.2f} ± {rew_ci95:.2f}")
tqdm_val.close()
test_env.close()
eval_log = {
'reward_per_step': r_avg_ps,
'reward_per_problem': r_avg_pp,
'rewards': rewards,
'problems_finished': problems_finished,
}
return eval_log
def evaluate(net, split='valid', subset=None):
test_env = SubprocVecEnv([lambda: gym.make('Sokograph-v0', split=split, subset=subset) for i in range(config.eval_batch)], in_series=(config.eval_batch // config.cpus), context='fork')
tqdm_val = tqdm(desc='Validating', total=config.eval_problems, unit=' steps')
with torch.no_grad():
net.eval()
r_tot = 0.
problems_solved = 0
problems_finished = 0
steps = 0
s = test_env.reset()
while problems_finished < config.eval_problems:
steps += 1
a, n, v, pi = net(s)
actions = to_action(a, n, s, size=config.soko_size)
s, r, d, i = test_env.step(actions)
# print(r)
r_tot += np.sum(r)
problems_solved += sum('all_boxes_on_target' in x and x['all_boxes_on_target'] == True for x in i)
problems_finished += np.sum(d)
tqdm_val.update()
r_avg = r_tot / (steps * config.eval_batch) # average reward per step
problems_solved_ps = problems_solved / (steps * config.eval_batch)
problems_solved_avg = problems_solved / problems_finished
net.train()
tqdm_val.close()
test_env.close()
return r_avg, problems_solved_ps, problems_solved_avg, problems_finished
def evaluate(net, planner):
test_env = SubprocVecEnv([
lambda: gym.make('Boxworld-v0', plan=planner)
for i in range(config.eval_batch)
],
in_series=(config.eval_batch // config.cpus),
context='fork')
tqdm_val = tqdm(desc='Validating',
total=config.eval_problems,
unit=' problems')
with torch.no_grad():
net.eval()
r_tot = 0.
problems_solved = 0.
problems_finished = 0.
problems_timeout = 0.
steps = 0
opt_all = []
opt_solved = []
s = test_env.reset()
while problems_finished < config.eval_problems:
steps += 1
# for step in range(1e9):
a, v, pi = net(s)
s, r, d, i = test_env.step(a)
# print(r)
r_tot += np.sum(r)
problems_solved += np.array(
sum(x['d_true'] for x in i)
) # conversion to numpy for easier ZeroDivision handling (-> nan)
problems_finished += np.sum(d)
if planner is not None:
# print([x['path_len'] / x['steps'] if x['d_true'] else 0. for x in i if x['done']])
opt_all += [
x['path_len'] / x['steps'] if x['d_true'] else 0.
for x in i if x['done']
]
opt_solved += [
x['path_len'] / x['steps'] for x in i if x['d_true']
]
tqdm_val.update(np.sum(d))
problems_solved_ps = problems_solved / (steps * config.eval_batch)
problems_solved_avg = problems_solved / problems_finished
r_avg_ps = r_tot / (steps * config.eval_batch
) # average reward per step
r_avg_pp = r_tot / problems_finished # average reward per problem
opt_all_avg = np.mean(opt_all)
opt_all_sem = scipy.stats.sem(opt_all)
opt_solved_avg = np.mean(opt_solved)
opt_solved_sem = scipy.stats.sem(opt_solved)
avg_steps_to_solve = (steps * config.eval_batch) / problems_finished
net.train()
tqdm_val.close()
test_env.close()
eval_log = {
'reward_per_step': r_avg_ps,
'reward_per_problem': r_avg_pp,
'problems_solved': problems_solved_avg,
'problems_finished': problems_finished,
'solved_per_step': problems_solved_ps,
'steps_per_problem': avg_steps_to_solve,
'optimality_all': opt_all_avg,
'optimality_all_sem': opt_all_sem,
'optimality_solved': opt_solved_avg,
'optimality_solved_sem': opt_solved_sem,
}
return eval_log
job_name = None
wandb.init(project="rrl-boxworld", name=job_name, config=config)
wandb.save("*.pt")
wandb.watch(net, log='all')
# print(net)
tot_env_steps = 0
tot_el_env_steps = 0
tqdm_main = tqdm(desc='Training', unit=' steps')
s = env.reset()
for step in itertools.count(start=1):
a, v, pi = net(s)
s, r, d, i = env.step(a)
# print(r, d)
# print(s)
s_true = [x['s_true'] for x in i]
d_true = [x['d_true'] for x in i]
n_stacks = list(len(x['raw_state'])
for x in i) # for the entropy regularization
# update network
loss, loss_pi, loss_v, loss_h, entropy, norm = net.update(
r, v, pi, s_true, n_stacks, d_true, target_net)
target_net.copy_weights(net, rho=config.target_rho)
# save step stats
def main():
os.environ['OMP_NUM_THREADS'] = '1'
envs = [
make_env(args.env_name, args.seed, i, args.log_dir)
for i in range(args.num_processes)
]
if args.num_processes > 1:
envs = SubprocVecEnv(envs)
else:
envs = DummyVecEnv(envs)
obs_shape = envs.observation_space.shape
obs_shape = (obs_shape[0] * args.num_stack, *obs_shape[1:])
obs_numel = reduce(operator.mul, obs_shape, 1)
actor_critic = Policy(obs_numel, envs.action_space)
# Maxime: log some info about the model and its size
modelSize = 0
for p in actor_critic.parameters():
pSize = reduce(operator.mul, p.size(), 1)
modelSize += pSize
print(str(actor_critic))
print('Total model size: %d' % modelSize)
if envs.action_space.__class__.__name__ == "Discrete":
action_shape = 1
else:
action_shape = envs.action_space.shape[0]
if args.cuda:
actor_critic.cuda()
if args.algo == 'a2c':
optimizer = optim.RMSprop(actor_critic.parameters(),
args.lr,
eps=args.eps,
alpha=args.alpha)
elif args.algo == 'ppo':
optimizer = optim.Adam(actor_critic.parameters(),
args.lr,
eps=args.eps)
elif args.algo == 'acktr':
optimizer = KFACOptimizer(actor_critic)
rollouts = RolloutStorage(args.num_steps, args.num_processes, obs_shape,
envs.action_space, actor_critic.state_size)
current_obs = torch.zeros(args.num_processes, *obs_shape)
def update_current_obs(obs):
shape_dim0 = envs.observation_space.shape[0]
obs = torch.from_numpy(obs).float()
if args.num_stack > 1:
current_obs[:, :-shape_dim0] = current_obs[:, shape_dim0:]
current_obs[:, -shape_dim0:] = obs
obs = envs.reset()
update_current_obs(obs)
rollouts.observations[0].copy_(current_obs)
# These variables are used to compute average rewards for all processes.
episode_rewards = torch.zeros([args.num_processes, 1])
final_rewards = torch.zeros([args.num_processes, 1])
if args.cuda:
current_obs = current_obs.cuda()
rollouts.cuda()
start = time.time()
for j in range(num_updates):
for step in range(args.num_steps):
# Sample actions
value, action, action_log_prob, states = actor_critic.act(
Variable(rollouts.observations[step], volatile=True),
Variable(rollouts.states[step], volatile=True),
Variable(rollouts.masks[step], volatile=True))
cpu_actions = action.data.squeeze(1).cpu().numpy()
# Obser reward and next obs
obs, reward, done, info = envs.step(cpu_actions)
reward = torch.from_numpy(np.expand_dims(np.stack(reward),
1)).float()
episode_rewards += reward
# If done then clean the history of observations.
masks = torch.FloatTensor([[0.0] if done_ else [1.0]
for done_ in done])
final_rewards *= masks
final_rewards += (1 - masks) * episode_rewards
episode_rewards *= masks
if args.cuda:
masks = masks.cuda()
if current_obs.dim() == 4:
current_obs *= masks.unsqueeze(2).unsqueeze(2)
elif current_obs.dim() == 3:
current_obs *= masks.unsqueeze(2)
else:
current_obs *= masks
update_current_obs(obs)
rollouts.insert(step, current_obs, states.data, action.data,
action_log_prob.data, value.data, reward, masks)
next_value = actor_critic(
Variable(rollouts.observations[-1], volatile=True),
Variable(rollouts.states[-1], volatile=True),
Variable(rollouts.masks[-1], volatile=True))[0].data
rollouts.compute_returns(next_value, args.use_gae, args.gamma,
args.tau)
if args.algo in ['a2c', 'acktr']:
values, action_log_probs, dist_entropy, states = actor_critic.evaluate_actions(
Variable(rollouts.observations[:-1].view(-1, *obs_shape)),
Variable(rollouts.states[:-1].view(-1,
actor_critic.state_size)),
Variable(rollouts.masks[:-1].view(-1, 1)),
Variable(rollouts.actions.view(-1, action_shape)))
values = values.view(args.num_steps, args.num_processes, 1)
action_log_probs = action_log_probs.view(args.num_steps,
args.num_processes, 1)
advantages = Variable(rollouts.returns[:-1]) - values
value_loss = advantages.pow(2).mean()
action_loss = -(Variable(advantages.data) *
action_log_probs).mean()
if args.algo == 'acktr' and optimizer.steps % optimizer.Ts == 0:
# Sampled fisher, see Martens 2014
actor_critic.zero_grad()
pg_fisher_loss = -action_log_probs.mean()
value_noise = Variable(torch.randn(values.size()))
if args.cuda:
value_noise = value_noise.cuda()
sample_values = values + value_noise
vf_fisher_loss = -(values -
Variable(sample_values.data)).pow(2).mean()
fisher_loss = pg_fisher_loss + vf_fisher_loss
optimizer.acc_stats = True
fisher_loss.backward(retain_graph=True)
optimizer.acc_stats = False
optimizer.zero_grad()
(value_loss * args.value_loss_coef + action_loss -
dist_entropy * args.entropy_coef).backward()
if args.algo == 'a2c':
nn.utils.clip_grad_norm(actor_critic.parameters(),
args.max_grad_norm)
optimizer.step()
elif args.algo == 'ppo':
advantages = rollouts.returns[:-1] - rollouts.value_preds[:-1]
advantages = (advantages - advantages.mean()) / (advantages.std() +
1e-5)
for e in range(args.ppo_epoch):
if args.recurrent_policy:
data_generator = rollouts.recurrent_generator(
advantages, args.num_mini_batch)
else:
data_generator = rollouts.feed_forward_generator(
advantages, args.num_mini_batch)
for sample in data_generator:
observations_batch, states_batch, actions_batch, \
return_batch, masks_batch, old_action_log_probs_batch, \
adv_targ = sample
# Reshape to do in a single forward pass for all steps
values, action_log_probs, dist_entropy, states = actor_critic.evaluate_actions(
Variable(observations_batch), Variable(states_batch),
Variable(masks_batch), Variable(actions_batch))
adv_targ = Variable(adv_targ)
ratio = torch.exp(action_log_probs -
Variable(old_action_log_probs_batch))
surr1 = ratio * adv_targ
surr2 = torch.clamp(ratio, 1.0 - args.clip_param,
1.0 + args.clip_param) * adv_targ
action_loss = -torch.min(
surr1,
surr2).mean() # PPO's pessimistic surrogate (L^CLIP)
value_loss = (Variable(return_batch) -
values).pow(2).mean()
optimizer.zero_grad()
(value_loss + action_loss -
dist_entropy * args.entropy_coef).backward()
nn.utils.clip_grad_norm(actor_critic.parameters(),
args.max_grad_norm)
optimizer.step()
rollouts.after_update()
if j % args.save_interval == 0 and args.save_dir != "":
save_path = os.path.join(args.save_dir, args.algo)
try:
os.makedirs(save_path)
except OSError:
pass
# A really ugly way to save a model to CPU
save_model = actor_critic
if args.cuda:
save_model = copy.deepcopy(actor_critic).cpu()
save_model = [
save_model,
hasattr(envs, 'ob_rms') and envs.ob_rms or None
]
torch.save(save_model,
os.path.join(save_path, args.env_name + ".pt"))
if j % args.log_interval == 0:
end = time.time()
total_num_steps = (j + 1) * args.num_processes * args.num_steps
print(
"Updates {}, num timesteps {}, FPS {}, mean/median reward {:.2f}/{:.2f}, min/max reward {:.2f}/{:.2f}, entropy {:.5f}, value loss {:.5f}, policy loss {:.5f}"
.format(j, total_num_steps,
int(total_num_steps / (end - start)),
final_rewards.mean(), final_rewards.median(),
final_rewards.min(), final_rewards.max(),
dist_entropy.data[0], value_loss.data[0],
action_loss.data[0]))
if args.vis and j % args.vis_interval == 0:
win = visdom_plot(total_num_steps, final_rewards.mean())
wandb.watch(net, log='all') # print(net) tot_env_steps = 0 tot_el_env_steps = 0 tqdm_main = tqdm(desc='Training', unit=' steps') s = env.reset() for step in itertools.count(start=1): a, n, v, pi = net(s) actions = to_action(a, n, s, size=config.soko_size) # print(actions) s, r, d, i = env.step(actions) s_true = [x['s_true'] for x in i] d_true = [x['d_true'] for x in i] # update network loss, loss_pi, loss_v, loss_h, entropy, norm = net.update(r, v, pi, s_true, d_true, target_net) target_net.copy_weights(net, rho=config.target_rho) # save step stats tot_env_steps += config.batch tot_el_env_steps += np.sum([x['elementary_steps'] for x in i]) tqdm_main.update() if step % config.sched_lr_rate == 0:
def main():
os.environ['OMP_NUM_THREADS'] = '1'
if args.vis:
from visdom import Visdom
viz = Visdom()
win = None
envs = [
make_env(args.env_name, args.seed, i, args.log_dir,
args.start_container) for i in range(args.num_processes)
]
test_envs = [
make_env(args.env_name, args.seed, i, args.log_dir,
args.start_container) for i in range(args.num_processes)
]
if args.num_processes > 1:
envs = SubprocVecEnv(envs)
test_envs = SubprocVecEnv(test_envs)
else:
envs = DummyVecEnv(envs)
test_envs = DummyVecEnv(test_envs)
obs_shape = envs.observation_space.shape
obs_shape = (obs_shape[0] * args.num_stack, *obs_shape[1:])
if args.saved_encoder_model:
obs_shape = (args.num_stack, args.latent_space_size)
obs_numel = reduce(operator.mul, obs_shape, 1)
if len(obs_shape) == 3 and obs_numel > 1024:
actor_critic = CNNPolicy(obs_shape[0], envs.action_space,
args.recurrent_policy)
else:
assert not args.recurrent_policy, \
"Recurrent policy is not implemented for the MLP controller"
actor_critic = MLPPolicy(obs_numel, envs.action_space)
modelSize = 0
for p in actor_critic.parameters():
pSize = reduce(operator.mul, p.size(), 1)
modelSize += pSize
print(str(actor_critic))
print('Total model size: %d' % modelSize)
if envs.action_space.__class__.__name__ == "Discrete":
action_shape = 1
else:
action_shape = envs.action_space.shape[0]
if args.resume_experiment:
print("\n############## Loading saved model ##############\n")
actor_critic, ob_rms = torch.load(
os.path.join(save_path, args.env_name + args.save_tag + ".pt"))
tr.load(os.path.join(log_path, args.env_name + args.save_tag + ".p"))
if args.cuda:
actor_critic.cuda()
if args.algo == 'a2c':
optimizer = optim.RMSprop(actor_critic.parameters(),
args.lr,
eps=args.eps,
alpha=args.alpha)
elif args.algo == 'ppo':
optimizer = optim.Adam(actor_critic.parameters(),
args.lr,
eps=args.eps)
elif args.algo == 'acktr':
optimizer = KFACOptimizer(actor_critic)
print(obs_shape)
rollouts = RolloutStorage(args.num_steps, args.num_processes, obs_shape,
envs.action_space, actor_critic.state_size)
rollouts_test = RolloutStorage(args.num_steps_test, args.num_processes,
obs_shape, envs.action_space,
actor_critic.state_size)
current_obs = torch.zeros(args.num_processes, *obs_shape)
current_obs_test = torch.zeros(args.num_processes, *obs_shape)
def update_current_obs(obs, test=False):
shape_dim0 = envs.observation_space.shape[0]
if args.saved_encoder_model:
shape_dim0 = 1
obs, _ = vae.encode(Variable(torch.cuda.FloatTensor(obs)))
obs = obs.data.cpu().numpy()
obs = torch.from_numpy(obs).float()
if not test:
if args.num_stack > 1:
current_obs[:, :-shape_dim0] = current_obs[:, shape_dim0:]
current_obs[:, -shape_dim0:] = obs
else:
if args.num_stack > 1:
current_obs_test[:, :
-shape_dim0] = current_obs_test[:,
shape_dim0:]
current_obs_test[:, -shape_dim0:] = obs
obs = envs.reset()
update_current_obs(obs)
rollouts.observations[0].copy_(current_obs)
# These variables are used to compute average rewards for all processes.
episode_rewards = torch.zeros([args.num_processes, 1])
final_rewards = torch.zeros([args.num_processes, 1])
reward_avg = 0
if args.cuda:
current_obs = current_obs.cuda()
current_obs_test = current_obs_test.cuda()
rollouts.cuda()
rollouts_test.cuda()
start = time.time()
for j in range(num_updates):
for step in range(args.num_steps):
# Sample actions
value, action, action_log_prob, states = actor_critic.act(
Variable(rollouts.observations[step], volatile=True),
Variable(rollouts.states[step], volatile=True),
Variable(rollouts.masks[step], volatile=True))
cpu_actions = action.data.squeeze(1).cpu().numpy()
# Observation, reward and next obs
obs, reward, done, info = envs.step(cpu_actions)
# Maxime: clip the reward within [0,1] for more reliable training
# This code deals poorly with large reward values
reward = np.clip(reward, a_min=0, a_max=None) / 400
reward = torch.from_numpy(np.expand_dims(np.stack(reward),
1)).float()
episode_rewards += reward
# If done then clean the history of observations.
masks = torch.FloatTensor([[0.0] if done_ else [1.0]
for done_ in done])
final_rewards *= masks
final_rewards += (1 - masks) * episode_rewards
episode_rewards *= masks
tr.episodes_done += args.num_processes - masks.sum()
if args.cuda:
masks = masks.cuda()
if current_obs.dim() == 4:
current_obs *= masks.unsqueeze(2).unsqueeze(2)
else:
current_obs *= masks
update_current_obs(obs)
rollouts.insert(step, current_obs, states.data, action.data,
action_log_prob.data, value.data, reward, masks)
next_value = actor_critic(
Variable(rollouts.observations[-1], volatile=True),
Variable(rollouts.states[-1], volatile=True),
Variable(rollouts.masks[-1], volatile=True))[0].data
rollouts.compute_returns(next_value, args.use_gae, args.gamma,
args.tau)
tr.iterations_done += 1
if args.algo in ['a2c', 'acktr']:
values, action_log_probs, dist_entropy, states = actor_critic.evaluate_actions(
Variable(rollouts.observations[:-1].view(-1, *obs_shape)),
Variable(rollouts.states[0].view(-1, actor_critic.state_size)),
Variable(rollouts.masks[:-1].view(-1, 1)),
Variable(rollouts.actions.view(-1, action_shape)))
values = values.view(args.num_steps, args.num_processes, 1)
action_log_probs = action_log_probs.view(args.num_steps,
args.num_processes, 1)
advantages = Variable(rollouts.returns[:-1]) - values
value_loss = advantages.pow(2).mean()
action_loss = -(Variable(advantages.data) *
action_log_probs).mean()
if args.algo == 'acktr' and optimizer.steps % optimizer.Ts == 0:
# Sampled fisher, see Martens 2014
actor_critic.zero_grad()
pg_fisher_loss = -action_log_probs.mean()
value_noise = Variable(torch.randn(values.size()))
if args.cuda:
value_noise = value_noise.cuda()
sample_values = values + value_noise
vf_fisher_loss = -(values -
Variable(sample_values.data)).pow(2).mean()
fisher_loss = pg_fisher_loss + vf_fisher_loss
optimizer.acc_stats = True
fisher_loss.backward(retain_graph=True)
optimizer.acc_stats = False
optimizer.zero_grad()
(value_loss * args.value_loss_coef + action_loss -
dist_entropy * args.entropy_coef).backward()
if args.algo == 'a2c':
nn.utils.clip_grad_norm(actor_critic.parameters(),
args.max_grad_norm)
optimizer.step()
elif args.algo == 'ppo':
advantages = rollouts.returns[:-1] - rollouts.value_preds[:-1]
advantages = (advantages - advantages.mean()) / (advantages.std() +
1e-5)
for e in range(args.ppo_epoch):
if args.recurrent_policy:
data_generator = rollouts.recurrent_generator(
advantages, args.num_mini_batch)
else:
data_generator = rollouts.feed_forward_generator(
advantages, args.num_mini_batch)
for sample in data_generator:
observations_batch, states_batch, actions_batch, \
return_batch, masks_batch, old_action_log_probs_batch, \
adv_targ = sample
# Reshape to do in a single forward pass for all steps
values, action_log_probs, dist_entropy, states = actor_critic.evaluate_actions(
Variable(observations_batch), Variable(states_batch),
Variable(masks_batch), Variable(actions_batch))
adv_targ = Variable(adv_targ)
ratio = torch.exp(action_log_probs -
Variable(old_action_log_probs_batch))
surr1 = ratio * adv_targ
surr2 = torch.clamp(ratio, 1.0 - args.clip_param,
1.0 + args.clip_param) * adv_targ
action_loss = -torch.min(
surr1,
surr2).mean() # PPO's pessimistic surrogate (L^CLIP)
value_loss = (Variable(return_batch) -
values).pow(2).mean()
optimizer.zero_grad()
(value_loss + action_loss -
dist_entropy * args.entropy_coef).backward()
nn.utils.clip_grad_norm(actor_critic.parameters(),
args.max_grad_norm)
optimizer.step()
rollouts.after_update()
if j % args.save_interval == 0 and args.save_dir != "":
# A really ugly way to save a model to CPU
save_model = actor_critic
if args.cuda:
save_model = copy.deepcopy(actor_critic).cpu()
save_model = [
save_model,
hasattr(envs, 'ob_rms') and envs.ob_rms or None
]
torch.save(
save_model,
os.path.join(save_path, args.env_name + args.save_tag + ".pt"))
total_test_reward_list = []
step_test_list = []
for _ in range(args.num_tests):
test_obs = test_envs.reset()
update_current_obs(test_obs, test=True)
rollouts_test.observations[0].copy_(current_obs_test)
step_test = 0
total_test_reward = 0
while step_test < args.num_steps_test:
value_test, action_test, action_log_prob_test, states_test = actor_critic.act(
Variable(rollouts_test.observations[step_test],
volatile=True),
Variable(rollouts_test.states[step_test],
volatile=True),
Variable(rollouts_test.masks[step_test],
volatile=True))
cpu_actions_test = action_test.data.squeeze(
1).cpu().numpy()
# Observation, reward and next obs
obs_test, reward_test, done_test, info_test = test_envs.step(
cpu_actions_test)
# masks here doesn't really matter, but still
masks_test = torch.FloatTensor(
[[0.0] if done_test_ else [1.0]
for done_test_ in done_test])
# Maxime: clip the reward within [0,1] for more reliable training
# This code deals poorly with large reward values
reward_test = np.clip(reward_test, a_min=0,
a_max=None) / 400
total_test_reward += reward_test[0]
reward_test = torch.from_numpy(
np.expand_dims(np.stack(reward_test), 1)).float()
update_current_obs(obs_test)
rollouts_test.insert(step_test, current_obs_test, states_test.data, action_test.data, action_log_prob_test.data,\
value_test.data, reward_test, masks_test)
step_test += 1
if done_test:
break
#rollouts_test.reset() # Need to reinitialise with .cuda(); don't forget
total_test_reward_list.append(total_test_reward)
step_test_list.append(step_test)
append_to(tr.test_reward, tr,
sum(total_test_reward_list) / args.num_tests)
append_to(tr.test_episode_len, tr,
sum(step_test_list) / args.num_tests)
logger.log_scalar_rl(
"test_reward", tr.test_reward[0], args.sliding_wsize,
[tr.episodes_done, tr.global_steps_done, tr.iterations_done])
logger.log_scalar_rl(
"test_episode_len", tr.test_episode_len[0], args.sliding_wsize,
[tr.episodes_done, tr.global_steps_done, tr.iterations_done])
# Saving all the MyContainer variables
tr.save(
os.path.join(log_path, args.env_name + args.save_tag + ".p"))
if j % args.log_interval == 0:
reward_avg = 0.99 * reward_avg + 0.01 * final_rewards.mean()
end = time.time()
tr.global_steps_done = (j +
1) * args.num_processes * args.num_steps
print(
"Updates {}, num timesteps {}, FPS {}, running avg reward {:.3f}, entropy {:.5f}, value loss {:.5f}, policy loss {:.5f}"
.format(j, tr.global_steps_done,
int(tr.global_steps_done / (end - start)), reward_avg,
dist_entropy.data[0], value_loss.data[0],
action_loss.data[0]))
append_to(tr.pg_loss, tr, action_loss.data[0])
append_to(tr.val_loss, tr, value_loss.data[0])
append_to(tr.entropy_loss, tr, dist_entropy.data[0])
append_to(tr.train_reward_avg, tr, reward_avg)
logger.log_scalar_rl(
"train_pg_loss", tr.pg_loss[0], args.sliding_wsize,
[tr.episodes_done, tr.global_steps_done, tr.iterations_done])
logger.log_scalar_rl(
"train_val_loss", tr.val_loss[0], args.sliding_wsize,
[tr.episodes_done, tr.global_steps_done, tr.iterations_done])
logger.log_scalar_rl(
"train_entropy_loss", tr.entropy_loss[0], args.sliding_wsize,
[tr.episodes_done, tr.global_steps_done, tr.iterations_done])
logger.log_scalar_rl(
"train_reward_avg", tr.train_reward_avg[0], args.sliding_wsize,
[tr.episodes_done, tr.global_steps_done, tr.iterations_done])
"""
print("Updates {}, num timesteps {}, FPS {}, mean/median reward {:.1f}/{:.1f}, min/max reward {:.1f}/{:.1f}, entropy {:.5f}, value loss {:.5f}, policy loss {:.5f}".
format(
j,
total_num_steps,
int(total_num_steps / (end - start)),
final_rewards.mean(),
final_rewards.median(),
final_rewards.min(),
final_rewards.max(), dist_entropy.data[0],
value_loss.data[0], action_loss.data[0])
)
"""
if args.vis and j % args.vis_interval == 0:
try:
# Sometimes monitor doesn't properly flush the outputs
win = visdom_plot(viz, win, args.log_dir, args.env_name,
args.algo)
except IOError:
pass