def main():
env = KukaGymEnv(renders=True, isDiscrete=False, maxSteps=10000000)
save_path = os.path.join("saves", "ddpg-")
os.makedirs(save_path, exist_ok=True)
device = torch.device("cuda")
act_net = model.DDPGActor(env.observation_space.shape[0],
env.action_space.shape[0]).to(device)
crt_net = model.D4PGCritic(env.observation_space.shape[0],
env.action_space.shape[0], N_ATOMS, Vmin,
Vmax).to(device)
print(act_net)
print(crt_net)
tgt_act_net = common.TargetNet(act_net)
tgt_crt_net = common.TargetNet(crt_net)
writer = SummaryWriter(comment="-d4pg_")
agent = model.AgentDDPG(act_net, device=device)
exp_source = experience.ExperienceSourceFirstLast(env,
agent,
gamma=GAMMA,
steps_count=REWARD_STEPS)
buffer = experience.ExperienceReplayBuffer(exp_source,
buffer_size=REPLAY_SIZE)
act_opt = optim.Adam(act_net.parameters(), lr=LEARNING_RATE)
crt_opt = optim.Adam(crt_net.parameters(), lr=LEARNING_RATE)
frame_idx = 0
best_reward = None
with common.RewardTracker(writer) as tracker:
with common.TBMeanTracker(writer, batch_size=10) as tb_tracker:
while True:
frame_idx += 1
#print("populate")
buffer.populate(1)
rewards_steps = exp_source.pop_rewards_steps()
#print(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) < 100:
continue
batch = buffer.sample(BATCH_SIZE)
#print("infer")
states_v, actions_v, rewards_v, dones_mask, last_states_v = common.unpack_batch_ddqn(
batch, device)
#print("train critic")# train critic
crt_opt.zero_grad()
crt_distr_v = crt_net(states_v, actions_v)
last_act_v = tgt_act_net.target_model(last_states_v)
last_distr_v = F.softmax(tgt_crt_net.target_model(
last_states_v, last_act_v),
dim=1)
proj_distr_v = distr_projection(last_distr_v,
rewards_v,
dones_mask,
gamma=GAMMA**REWARD_STEPS,
device=device)
prob_dist_v = -F.log_softmax(crt_distr_v, dim=1) * proj_distr_v
critic_loss_v = prob_dist_v.sum(dim=1).mean()
critic_loss_v.backward()
crt_opt.step()
tb_tracker.track("loss_critic", critic_loss_v, frame_idx)
#print("train actor")
# train actor
act_opt.zero_grad()
act_opt.zero_grad()
cur_actions_v = act_net(states_v)
crt_distr_v = crt_net(states_v, cur_actions_v)
actor_loss_v = -crt_net.distr_to_q(crt_distr_v)
actor_loss_v = actor_loss_v.mean()
actor_loss_v.backward()
act_opt.step()
tb_tracker.track("loss_actor", actor_loss_v, frame_idx)
tgt_act_net.alpha_sync(alpha=1 - 1e-3)
tgt_crt_net.alpha_sync(alpha=1 - 1e-3)
if frame_idx % TEST_ITERS == 0:
print("testing")
env.reset()
ts = time.time()
rewards, steps = test_net(act_net, 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
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
# Entnahme einer Stichprobe aus dem Buffer
batch = buffer.sample(BATCH_SIZE)
states_v, actions_v, rewards_v, \
dones_mask, last_states_v = \
common.unpack_batch_ddqn(batch, device)
# Optimieren des Critic-Netzes
crt_opt.zero_grad()
crt_distr_v = crt_net(states_v, actions_v)
last_act_v = tgt_act_net.target_model(
last_states_v)
last_distr_v = F.softmax(
tgt_crt_net.target_model(
last_states_v, last_act_v), dim=1)
proj_distr_v = distr_projection(
last_distr_v, rewards_v, dones_mask,
gamma=GAMMA**REWARD_STEPS, device=device)
prob_dist_v = -F.log_softmax(
crt_distr_v, dim=1) * proj_distr_v
# Berechnen der Verlustfunktion des Critic-Netzes
with ptan.common.utils.TBMeanTracker(writer,
batch_size=10) as tb_tracker:
while frame_idx < MAX_IDX:
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, rewards_v, dones_mask, last_states_v = common.unpack_batch_ddqn(
batch, device)
# train critic
crt_opt.zero_grad()
q_v = crt_net(states_v, actions_v)
last_act_v = tgt_act_net.target_model(last_states_v)
q_last_v = tgt_crt_net.target_model(last_states_v, last_act_v)
q_last_v[dones_mask] = 0.0
q_ref_v = rewards_v.unsqueeze(
dim=-1) + q_last_v * (GAMMA**args.n)
critic_loss_v = F.mse_loss(q_v, q_ref_v.detach())
critic_loss_v.backward()
crt_opt.step()
tb_tracker.track("loss_critic", critic_loss_v, frame_idx)
tb_tracker.track("critic_ref", q_ref_v.mean(), frame_idx)
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, rewards_v, dones_mask, last_states_v = \
common.unpack_batch_ddqn(batch, cuda=args.cuda)
# train critic
crt_opt.zero_grad()
crt_distr_v = crt_net(states_v, actions_v)
last_act_v = tgt_act_net.target_model(last_states_v)
last_distr_v = F.softmax(tgt_crt_net.target_model(
last_states_v, last_act_v),
dim=1)
proj_distr_v = distr_projection(last_distr_v,
rewards_v,
dones_mask,
gamma=GAMMA**REWARD_STEPS,
cuda=args.cuda)
prob_dist_v = -F.log_softmax(crt_distr_v, dim=1) * proj_distr_v
critic_loss_v = prob_dist_v.sum(dim=1).mean()
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, rewards_v, dones_mask, last_states_v = common.unpack_batch_ddqn(batch, device)
# train critic
crt_opt.zero_grad()
q_v = crt_net(states_v, actions_v)
last_act_v = tgt_act_net.target_model(last_states_v)
q_last_v = tgt_crt_net.target_model(last_states_v, last_act_v)
q_last_v[dones_mask] = 0.0
q_ref_v = rewards_v.unsqueeze(dim=-1) + q_last_v * GAMMA
critic_loss_v = F.mse_loss(q_v, q_ref_v.detach())
critic_loss_v.backward()
crt_opt.step()
tb_tracker.track("loss_critic", critic_loss_v, frame_idx)
tb_tracker.track("critic_ref", q_ref_v.mean(), frame_idx)
# train actor
