def main():
parser = argparse.ArgumentParser()
parser.add_argument("--cuda",
default=False,
action='store_true',
help='Enable CUDA')
parser.add_argument("-n", "--name", required=True, help="Name of the run")
parser.add_argument("-e",
"--env",
default=ENV_ID,
help="Environment id, default=" + ENV_ID)
parser.add_argument("--lrc",
default=LEARNING_RATE_CRITIC,
type=float,
help="Critic learning rate")
parser.add_argument("--lra",
default=LEARNING_RATE_ACTOR,
type=float,
help="Actor learning rate")
args = parser.parse_args()
device = torch.device("cuda" if args.cuda else "cpu")
save_path = os.path.join("saves", "ppo-" + args.name)
os.makedirs(save_path, exist_ok=True)
env = gym.make(args.env)
test_env = gym.make(args.env)
net_act = model.ModelActor(env.observation_space.shape[0],
env.action_space.shape[0]).to(device)
net_crt = model.ModelCritic(env.observation_space.shape[0]).to(device)
print(net_act)
print(net_crt)
writer = SummaryWriter(comment="-ppo_" + args.name)
agent = model.AgentA2C(net_act, device=device)
exp_source = ptan.experience.ExperienceSource(env, agent, steps_count=1)
opt_act = optim.Adam(net_act.parameters(), lr=args.lra)
opt_crt = optim.Adam(net_crt.parameters(), lr=args.lrc)
trajectory = []
best_reward = None
with ptan.common.utils.RewardTracker(writer) as tracker:
for step_idx, exp in enumerate(exp_source):
rewards_steps = exp_source.pop_rewards_steps()
if rewards_steps:
rewards, steps = zip(*rewards_steps)
writer.add_scalar("episode_steps", np.mean(steps), step_idx)
tracker.reward(np.mean(rewards), step_idx)
if step_idx % TEST_ITERS == 0:
ts = time.time()
rewards, steps = test_net(net_act, test_env, device=device)
print("Test done in %.2f sec, reward %.3f, steps %d" %
(time.time() - ts, rewards, steps))
writer.add_scalar("test_reward", rewards, step_idx)
writer.add_scalar("test_steps", steps, step_idx)
if best_reward is None or best_reward < rewards:
if best_reward is not None:
print("Best reward updated: %.3f -> %.3f" %
(best_reward, rewards))
name = "best_%+.3f_%d.dat" % (rewards, step_idx)
fname = os.path.join(save_path, name)
torch.save(net_act.state_dict(), fname)
best_reward = rewards
trajectory.append(exp)
if len(trajectory) < TRAJECTORY_SIZE:
continue
traj_states = [t[0].state for t in trajectory]
traj_actions = [t[0].action for t in trajectory]
traj_states_v = torch.FloatTensor(traj_states)
traj_states_v = traj_states_v.to(device)
traj_actions_v = torch.FloatTensor(traj_actions)
traj_actions_v = traj_actions_v.to(device)
traj_adv_v, traj_ref_v = calc_adv_ref(trajectory,
net_crt,
traj_states_v,
device=device)
mu_v = net_act(traj_states_v)
old_logprob_v = calc_logprob(mu_v, net_act.logstd, traj_actions_v)
# normalize advantages
traj_adv_v = traj_adv_v - torch.mean(traj_adv_v)
traj_adv_v /= torch.std(traj_adv_v)
# drop last entry from the trajectory, an our adv and ref value calculated without it
trajectory = trajectory[:-1]
old_logprob_v = old_logprob_v[:-1].detach()
sum_loss_value = 0.0
sum_loss_policy = 0.0
count_steps = 0
for epoch in range(PPO_EPOCHES):
for batch_ofs in range(0, len(trajectory), PPO_BATCH_SIZE):
batch_l = batch_ofs + PPO_BATCH_SIZE
states_v = traj_states_v[batch_ofs:batch_l]
actions_v = traj_actions_v[batch_ofs:batch_l]
batch_adv_v = traj_adv_v[batch_ofs:batch_l]
batch_adv_v = batch_adv_v.unsqueeze(-1)
batch_ref_v = traj_ref_v[batch_ofs:batch_l]
batch_old_logprob_v = \
old_logprob_v[batch_ofs:batch_l]
# critic training
opt_crt.zero_grad()
value_v = net_crt(states_v)
loss_value_v = F.mse_loss(value_v.squeeze(-1), batch_ref_v)
loss_value_v.backward()
opt_crt.step()
# actor training
opt_act.zero_grad()
mu_v = net_act(states_v)
logprob_pi_v = calc_logprob(mu_v, net_act.logstd,
actions_v)
ratio_v = torch.exp(logprob_pi_v - batch_old_logprob_v)
surr_obj_v = batch_adv_v * ratio_v
c_ratio_v = torch.clamp(ratio_v, 1.0 - PPO_EPS,
1.0 + PPO_EPS)
clipped_surr_v = batch_adv_v * c_ratio_v
loss_policy_v = -torch.min(surr_obj_v,
clipped_surr_v).mean()
loss_policy_v.backward()
opt_act.step()
sum_loss_value += loss_value_v.item()
sum_loss_policy += loss_policy_v.item()
count_steps += 1
trajectory.clear()
writer.add_scalar("advantage", traj_adv_v.mean().item(), step_idx)
writer.add_scalar("values", traj_ref_v.mean().item(), step_idx)
writer.add_scalar("loss_policy", sum_loss_policy / count_steps,
step_idx)
writer.add_scalar("loss_value", sum_loss_value / count_steps,
step_idx)
best_reward = rewards
trajectory.append(exp)
if len(trajectory) < TRAJECTORY_SIZE:
continue
traj_states = [t[0].state for t in trajectory]
traj_actions = [t[0].action for t in trajectory]
traj_states_v = torch.FloatTensor(traj_states).to(device)
traj_actions_v = torch.FloatTensor(traj_actions).to(device)
traj_adv_v, traj_ref_v = calc_adv_ref(trajectory,
net_crt,
traj_states_v,
device=device)
mu_v = net_act(traj_states_v)
old_logprob_v = calc_logprob(mu_v, net_act.logstd, traj_actions_v)
# normalize advantages
traj_adv_v = (traj_adv_v -
torch.mean(traj_adv_v)) / torch.std(traj_adv_v)
# drop last entry from the trajectory, an our adv and ref value calculated without it
trajectory = trajectory[:-1]
old_logprob_v = old_logprob_v[:-1].detach()
traj_states_v = traj_states_v[:-1]
traj_actions_v = traj_actions_v[:-1]
sum_loss_value = 0.0
sum_loss_policy = 0.0
count_steps = 0
# critic step
def get_loss():
mu_v = net_act(traj_states_v)
logprob_v = calc_logprob(mu_v, net_act.logstd, traj_actions_v)
dp_v = torch.exp(logprob_v - old_logprob_v)
action_loss_v = -traj_adv_v.unsqueeze(dim=-1) * dp_v
return action_loss_v.mean()
continue
states_v, actions_v, vals_ref_v = \
common.unpack_batch_a2c(batch, net_crt, last_val_gamma=GAMMA ** REWARD_STEPS, device=device)
batch.clear()
opt_crt.zero_grad()
value_v = net_crt(states_v)
loss_value_v = F.mse_loss(value_v.squeeze(-1), vals_ref_v)
loss_value_v.backward()
opt_crt.step()
opt_act.zero_grad()
mu_v = net_act(states_v)
adv_v = vals_ref_v.unsqueeze(dim=-1) - value_v.detach()
log_prob_v = adv_v * calc_logprob(mu_v, net_act.logstd,
actions_v)
loss_policy_v = -log_prob_v.mean()
entropy_loss_v = ENTROPY_BETA * (
-(torch.log(2 * math.pi * torch.exp(net_act.logstd)) + 1) /
2).mean()
loss_v = loss_policy_v + entropy_loss_v
loss_v.backward()
opt_act.step()
tb_tracker.track("advantage", adv_v, step_idx)
tb_tracker.track("values", value_v, step_idx)
tb_tracker.track("batch_rewards", vals_ref_v, step_idx)
tb_tracker.track("loss_entropy", entropy_loss_v, step_idx)
tb_tracker.track("loss_policy", loss_policy_v, step_idx)
tb_tracker.track("loss_value", loss_value_v, step_idx)
tb_tracker.track("loss_total", loss_v, step_idx)