acts_v = net_act(states_v)
q_out_v, _ = twinq_net(states_v, acts_v)
act_loss = -q_out_v.mean()
act_loss.backward()
act_opt.step()
tb_tracker.track("loss_act", act_loss, frame_idx)
tgt_net_crt.alpha_sync(alpha=1 - 1e-3)
tcurr = time.time()
if (tcurr - tstart) >= maxsec:
break
if frame_idx % args.test_iters == 0:
rewards, steps = test_net(net_act, test_env, device=device)
print("Test done in %.2f sec, reward %.3f, steps %d" %
(time.time() - tcurr, rewards, steps))
writer.add_scalar("test_reward", rewards, frame_idx)
writer.add_scalar("test_steps", steps, frame_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, frame_idx)
fname = os.path.join(save_path, name)
torch.save(net_act.state_dict(), fname)
best_reward = rewards
pass
tb_tracker.track("loss_v", v_loss_v, frame_idx)
# Actor
act_opt.zero_grad()
acts_v = act_net(states_v)
q_out_v, _ = twinq_net(states_v, acts_v)
act_loss = -q_out_v.mean()
act_loss.backward()
act_opt.step()
tb_tracker.track("loss_act", act_loss, frame_idx)
tgt_crt_net.alpha_sync(alpha=1 - 1e-3)
if frame_idx % TEST_ITERS == 0:
ts = time.time()
rewards, steps = test_net(act_net, 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, frame_idx)
writer.add_scalar("test_steps", steps, frame_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, frame_idx)
fname = os.path.join(save_path, name)
torch.save(act_net.state_dict(), fname)
best_reward = rewards
pass
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)
def train(test_env, args):
device = torch.device("cuda" if args.cuda else "cpu")
save_path = os.path.join("saves", "sac-" + args.name)
os.makedirs(save_path, exist_ok=True)
env = gym.make(args.env)
test_env = gym.make(args.env)
act_net = model.ModelActor(env.observation_space.shape[0],
env.action_space.shape[0]).to(device)
crt_net = model.ModelCritic(env.observation_space.shape[0]).to(device)
twinq_net = model.ModelSACTwinQ(env.observation_space.shape[0],
env.action_space.shape[0]).to(device)
print(act_net)
print(crt_net)
print(twinq_net)
tgt_crt_net = ptan.agent.TargetNet(crt_net)
writer = SummaryWriter(comment="-sac_" + args.name)
agent = model.AgentDDPG(act_net, device=device)
exp_source = ptan.experience.ExperienceSourceFirstLast(env,
agent,
gamma=GAMMA,
steps_count=1)
buffer = ptan.experience.ExperienceReplayBuffer(exp_source,
buffer_size=REPLAY_SIZE)
act_opt = optim.Adam(act_net.parameters(), lr=LR_ACTS)
crt_opt = optim.Adam(crt_net.parameters(), lr=LR_VALS)
twinq_opt = optim.Adam(twinq_net.parameters(), lr=LR_VALS)
frame_idx = 0
best_reward = None
with ptan.common.utils.RewardTracker(writer) as tracker:
with ptan.common.utils.TBMeanTracker(writer,
batch_size=10) as tb_tracker:
while True:
frame_idx += 1
buffer.populate(1)
rewards_steps = exp_source.pop_rewards_steps()
if rewards_steps:
rewards, steps = zip(*rewards_steps)
tb_tracker.track("episode_steps", steps[0], frame_idx)
tracker.reward(rewards[0], frame_idx)
if len(buffer) < REPLAY_INITIAL:
continue
batch = buffer.sample(BATCH_SIZE)
states_v, actions_v, ref_vals_v, ref_q_v = \
common.unpack_batch_sac(
batch, tgt_crt_net.target_model,
twinq_net, act_net, GAMMA,
SAC_ENTROPY_ALPHA, device)
tb_tracker.track("ref_v", ref_vals_v.mean(), frame_idx)
tb_tracker.track("ref_q", ref_q_v.mean(), frame_idx)
# train TwinQ
twinq_opt.zero_grad()
q1_v, q2_v = twinq_net(states_v, actions_v)
q1_loss_v = F.mse_loss(q1_v.squeeze(), ref_q_v.detach())
q2_loss_v = F.mse_loss(q2_v.squeeze(), ref_q_v.detach())
q_loss_v = q1_loss_v + q2_loss_v
q_loss_v.backward()
twinq_opt.step()
tb_tracker.track("loss_q1", q1_loss_v, frame_idx)
tb_tracker.track("loss_q2", q2_loss_v, frame_idx)
# Critic
crt_opt.zero_grad()
val_v = crt_net(states_v)
v_loss_v = F.mse_loss(val_v.squeeze(), ref_vals_v.detach())
v_loss_v.backward()
crt_opt.step()
tb_tracker.track("loss_v", v_loss_v, frame_idx)
# Actor
act_opt.zero_grad()
acts_v = act_net(states_v)
q_out_v, _ = twinq_net(states_v, acts_v)
act_loss = -q_out_v.mean()
act_loss.backward()
act_opt.step()
tb_tracker.track("loss_act", act_loss, frame_idx)
tgt_crt_net.alpha_sync(alpha=1 - 1e-3)
if frame_idx % TEST_ITERS == 0:
ts = time.time()
rewards, steps = test_net(act_net, 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, frame_idx)
writer.add_scalar("test_steps", steps, frame_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, frame_idx)
fname = os.path.join(save_path, name)
torch.save(act_net.state_dict(), fname)
best_reward = rewards
pass
