def main():
# initialize the game
env = gym.make('Pendulum-v0').unwrapped
print env.observation_space
print env.observation_space.high
print env.observation_space.low
print env.action_space
# import hyper parameters
args = init_hyper_para()
# random initialize critic network
state_dim = env.reset().shape[0]
action_dim = env.action_space.shape[0]
# if we have the saved model, load it
if os.path.exists(
'/home/likang/PycharmProjects/myddpg/bin/Models/critic.ckpt'):
critic_net = torch.load(
'/home/likang/PycharmProjects/myddpg/bin/Models/critic.ckpt')
else: # initialize the model
critic_net = net.CriticNetwork(
state_dim=state_dim, action_dim=action_dim).to(
device) # need to init paras according to the gym game
# random initialize actor network(also called policy network)
if os.path.exists(
'/home/likang/PycharmProjects/myddpg/bin/Models/actor.ckpt'):
actor_net = torch.load(
'/home/likang/PycharmProjects/myddpg/bin/Models/actor.ckpt')
else:
actor_net = net.ActorNetwork(state_dim=state_dim,
action_dim=action_dim).to(device)
# initialize
optimizer_critic = opt.Adam(critic_net.parameters(), lr=0.001)
optimizer_actor = opt.Adam(actor_net.parameters(), lr=0.001)
# initialize target critic network which is the same of critic network
target_critic_net = copy.deepcopy(critic_net)
# initialize target actor network which is the same of actor network
target_actor_net = copy.deepcopy(actor_net)
# init the memory buffer
memory = Memory(args.capacity)
# initialize a random process N for action exploration
ounoise = OUNoise(env.action_space.shape[0]) # init random process
# enter circle of training process
for ep in range(args.num_ep):
print(["ep: ", ep])
# reset random process
ounoise.scale = (args.noise_scale - args.final_noise_scale) * max(
0, args.exploration_end -
ep) / args.exploration_end + args.final_noise_scale
ounoise.reset()
# initialize a state s1
state = env.reset() # 这里把state初始化成二维的tensor
state = torch.tensor([state], dtype=torch.float32).to(device)
for t in range(MAX_STEP):
print(['time step: ', t])
# select a action according to actor network(also called policy network)
action = actor_net.select_action(state, ounoise)
# execute the action and get a new state s_i+i
# get a reward from the environment
next_state, reward, done, _ = env.step([action.item()])
# store the transition {s_i, a_i, r_i, s_i+1} into memory
next_state = torch.tensor([next_state],
device=device,
dtype=torch.float32)
reward = torch.tensor([[reward]],
device=device,
dtype=torch.float32)
memory.push(state, action, reward, next_state)
state = next_state
# print([state, action, reward, next_state])
del action, reward, next_state
# get a batch_size transitions.
# (s_i, a_i, r_i, s_{i+1}) in Algorithm1 of DDPG
transitions = memory.sample(args.batch_size)
s1 = torch.cat([tran.state for tran in transitions])
s2 = torch.cat([tran.next_state for tran in transitions])
r1 = torch.cat([tran.reward for tran in transitions])
a1 = torch.cat([tran.action for tran in transitions])
update_critic_net(s1, s2, r1, a1, target_actor_net,
target_critic_net, critic_net, optimizer_critic,
args)
# update actor policy network
update_actor_net(s1, actor_net, critic_net, optimizer_actor)
# update target critic network
# theta^{Q'}, see algorithm1 of DDPG
for target_param, source_param in zip(
target_critic_net.parameters(), critic_net.parameters()):
target_param.data.copy_(args.tau * source_param +
(1 - args.tau) * target_param)
# update target actor network
# theta^{mu'}, see algorithm1 of DDPG
for target_param, source_param in zip(
target_actor_net.parameters(), actor_net.parameters()):
target_param.data.copy_(args.tau * source_param +
(1 - args.tau) * target_param)
# show image
plt.imshow(env.render('rgb_array'))
time.sleep(0.001)
# finish
if done:
break
del transitions
gc.collect()
if ep % 10 == 0: # save model
torch.save(critic_net, './Models/' + 'critic.ckpt')
torch.save(actor_net, './Models/' + 'actor.ckpt')
def main(cfg):
random.seed(cfg.exp.seed)
np.random.seed(cfg.exp.seed)
torch.manual_seed(cfg.exp.seed)
torch.backends.cudnn.deterministic = cfg.exp.torch_deterministic
# so that the environment automatically resets
env = SyncVectorEnv([
lambda: RecordEpisodeStatistics(gym.make('CartPole-v1'))
])
actor, critic = Actor(), Critic()
actor_optim = Adam(actor.parameters(), eps=1e-5, lr=cfg.params.actor_lr)
critic_optim = Adam(critic.parameters(), eps=1e-5, lr=cfg.params.critic_lr)
memory = Memory(mini_batch_size=cfg.params.mini_batch_size, batch_size=cfg.params.batch_size)
obs = env.reset()
global_rewards = []
NUM_UPDATES = (cfg.params.total_timesteps // cfg.params.batch_size) * cfg.params.epochs
cur_timestep = 0
def calc_factor(cur_timestep: int) -> float:
"""Calculates the factor to be multiplied with the learning rate to update it."""
update_number = cur_timestep // cfg.params.batch_size
total_updates = cfg.params.total_timesteps // cfg.params.batch_size
fraction = 1.0 - update_number / total_updates
return fraction
actor_scheduler = LambdaLR(actor_optim, lr_lambda=calc_factor, verbose=True)
critic_scheduler = LambdaLR(critic_optim, lr_lambda=calc_factor, verbose=True)
while cur_timestep < cfg.params.total_timesteps:
# keep playing the game
obs = torch.as_tensor(obs, dtype=torch.float32)
with torch.no_grad():
dist = actor(obs)
action = dist.sample()
log_prob = dist.log_prob(action)
value = critic(obs)
action = action.cpu().numpy()
value = value.cpu().numpy()
log_prob = log_prob.cpu().numpy()
obs_, reward, done, info = env.step(action)
if done[0]:
tqdm.write(f'Reward: {info[0]["episode"]["r"]}, Avg Reward: {np.mean(global_rewards[-10:]):.3f}')
global_rewards.append(info[0]['episode']['r'])
wandb.log({'Avg_Reward': np.mean(global_rewards[-10:]), 'Reward': info[0]['episode']['r']})
memory.remember(obs.squeeze(0).cpu().numpy(), action.item(), log_prob.item(), reward.item(), done.item(), value.item())
obs = obs_
cur_timestep += 1
# if the current timestep is a multiple of the batch size, then we need to update the model
if cur_timestep % cfg.params.batch_size == 0:
for epoch in tqdm(range(cfg.params.epochs), desc=f'Num updates: {cfg.params.epochs * (cur_timestep // cfg.params.batch_size)} / {NUM_UPDATES}'):
# sample a batch from memory of experiences
old_states, old_actions, old_log_probs, old_rewards, old_dones, old_values, batch_indices = memory.sample()
old_log_probs = torch.tensor(old_log_probs, dtype=torch.float32)
old_actions = torch.tensor(old_actions, dtype=torch.float32)
advantage = calculate_advantage(old_rewards, old_values, old_dones, gae_gamma=cfg.params.gae_gamma, gae_lambda=cfg.params.gae_lambda)
advantage = torch.tensor(advantage, dtype=torch.float32)
old_rewards = torch.tensor(old_rewards, dtype=torch.float32)
old_values = torch.tensor(old_values, dtype=torch.float32)
# for each mini batch from batch, calculate advantage using GAE
for mini_batch_index in batch_indices:
# remember: Normalization of advantage is done on mini batch, not the entire batch
advantage[mini_batch_index] = (advantage[mini_batch_index] - advantage[mini_batch_index].mean()) / (advantage[mini_batch_index].std() + 1e-8)
dist = actor(torch.tensor(old_states[mini_batch_index], dtype=torch.float32).unsqueeze(0))
# actions = dist.sample()
log_probs = dist.log_prob(old_actions[mini_batch_index]).squeeze(0)
entropy = dist.entropy().squeeze(0)
log_ratio = log_probs - old_log_probs[mini_batch_index]
ratio = torch.exp(log_ratio)
with torch.no_grad():
# approx_kl = ((ratio-1)-log_ratio).mean()
approx_kl = ((old_log_probs[mini_batch_index] - log_probs)**2).mean()
wandb.log({'Approx_KL': approx_kl})
actor_loss = -torch.min(
ratio * advantage[mini_batch_index],
torch.clamp(ratio, 1 - cfg.params.actor_loss_clip, 1 + cfg.params.actor_loss_clip) * advantage[mini_batch_index]
).mean()
values = critic(torch.tensor(old_states[mini_batch_index], dtype=torch.float32).unsqueeze(0)).squeeze(-1)
returns = old_values[mini_batch_index] + advantage[mini_batch_index]
critic_loss = torch.max(
(values - returns)**2,
(old_values[mini_batch_index] + torch.clamp(
values - old_values[mini_batch_index], -cfg.params.critic_loss_clip, cfg.params.critic_loss_clip
) - returns
)**2
).mean()
# critic_loss = F.mse_loss(values, returns)
wandb.log({'Actor_Loss': actor_loss.item(), 'Critic_Loss': critic_loss.item(), 'Entropy': entropy.mean().item()})
loss = actor_loss + 0.25 * critic_loss - 0.01 * entropy.mean()
actor_optim.zero_grad()
critic_optim.zero_grad()
loss.backward()
nn.utils.clip_grad_norm_(actor.parameters(), cfg.params.max_grad_norm)
nn.utils.clip_grad_norm_(critic.parameters(), cfg.params.max_grad_norm)
actor_optim.step()
critic_optim.step()
memory.reset()
actor_scheduler.step(cur_timestep)
critic_scheduler.step(cur_timestep)
y_pred, y_true = old_values.cpu().numpy(), (old_values + advantage).cpu().numpy()
var_y = np.var(y_true)
explained_var = np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y
wandb.log({'Explained_Var': explained_var})
if cfg.exp.save_weights:
torch.save(actor.state_dict(), Path(f'{hydra.utils.get_original_cwd()}/{cfg.exp.model_dir}/actor.pth'))
torch.save(critic.state_dict(), Path(f'{hydra.utils.get_original_cwd()}/{cfg.exp.model_dir}/critic.pth'))
