def init(self, net_dim, state_dim, action_dim, if_per=False):
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.compute_reward = self.compute_reward_gae if self.if_use_gae else self.compute_reward_adv
self.cri = CriticAdv(state_dim, net_dim, self.if_use_dn).to(self.device)
self.act = ActorPPO(net_dim, state_dim, action_dim, self.if_use_dn).to(self.device)
self.optimizer = torch.optim.Adam([{'params': self.act.parameters(), 'lr': self.learning_rate},
{'params': self.cri.parameters(), 'lr': self.learning_rate}])
self.criterion = torch.nn.SmoothL1Loss()
assert if_per is False # on-policy don't need PER
def __init__(self, net_dim, state_dim, action_dim, learning_rate=1e-4):
super().__init__()
self.clip = 0.25 # ratio.clamp(1 - clip, 1 + clip)
self.lambda_entropy = 0.01 # larger lambda_entropy means more exploration
self.act = ActorPPO(net_dim, state_dim, action_dim).to(self.device)
self.cri = CriticAdv(state_dim, net_dim).to(self.device)
self.criterion = torch.nn.SmoothL1Loss()
self.optimizer = torch.optim.Adam([{
'params': self.act.parameters(),
'lr': learning_rate
}, {
'params': self.cri.parameters(),
'lr': learning_rate
}])
class AgentPPO(AgentBase):
def __init__(self):
super().__init__()
self.ratio_clip = 0.3 # could be 0.2 ~ 0.5, ratio.clamp(1 - clip, 1 + clip),
self.lambda_entropy = 0.04 # could be 0.01 ~ 0.05
self.lambda_gae_adv = 0.97 # could be 0.95 ~ 0.99, GAE (Generalized Advantage Estimation. ICLR.2016.)
self.if_use_gae = False # if use Generalized Advantage Estimation
self.if_on_policy = True # AgentPPO is an on policy DRL algorithm
self.if_use_dn = False
self.noise = None
self.optimizer = None
self.compute_reward = None # attribution
def init(self, net_dim, state_dim, action_dim, if_per=False):
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.compute_reward = self.compute_reward_gae if self.if_use_gae else self.compute_reward_adv
self.cri = CriticAdv(state_dim, net_dim, self.if_use_dn).to(self.device)
self.act = ActorPPO(net_dim, state_dim, action_dim, self.if_use_dn).to(self.device)
self.optimizer = torch.optim.Adam([{'params': self.act.parameters(), 'lr': self.learning_rate},
{'params': self.cri.parameters(), 'lr': self.learning_rate}])
self.criterion = torch.nn.SmoothL1Loss()
assert if_per is False # on-policy don't need PER
def select_action(self, state) -> tuple:
"""select action for PPO
:array state: state.shape==(state_dim, )
:return array action: state.shape==(action_dim, )
:return array noise: noise.shape==(action_dim, ), the noise
"""
states = torch.as_tensor((state,), dtype=torch.float32, device=self.device).detach()
actions, noises = self.act.get_action_noise(states)
return actions[0].cpu().numpy(), noises[0].cpu().numpy()
def explore_env(self, env, buffer, target_step, reward_scale, gamma) -> int:
buffer.empty_buffer_before_explore() # NOTICE! necessary for on-policy
# assert target_step == buffer.max_len - max_step
actual_step = 0
while actual_step < target_step:
state = env.reset()
for _ in range(env.max_step):
action, noise = self.select_action(state)
next_state, reward, done, _ = env.step(np.tanh(action))
actual_step += 1
other = (reward * reward_scale, 0.0 if done else gamma, *action, *noise)
buffer.append_buffer(state, other)
if done:
break
state = next_state
return actual_step
def update_net(self, buffer, _target_step, batch_size, repeat_times=4) -> (float, float):
buffer.update_now_len_before_sample()
buf_len = buffer.now_len # assert buf_len >= _target_step
'''Trajectory using reverse reward'''
with torch.no_grad():
buf_reward, buf_mask, buf_action, buf_noise, buf_state = buffer.sample_all()
bs = 2 ** 10 # set a smaller 'bs: batch size' when out of GPU memory.
buf_value = torch.cat([self.cri(buf_state[i:i + bs]) for i in range(0, buf_state.size(0), bs)], dim=0)
buf_logprob = -(buf_noise.pow(2).__mul__(0.5) + self.act.a_std_log + self.act.sqrt_2pi_log).sum(1)
buf_r_sum, buf_advantage = self.compute_reward(buf_len, buf_reward, buf_mask, buf_value)
del buf_reward, buf_mask, buf_noise
'''PPO: Surrogate objective of Trust Region'''
obj_critic = None
for _ in range(int(repeat_times * buf_len / batch_size)):
indices = torch.randint(buf_len, size=(batch_size,), requires_grad=False, device=self.device)
state = buf_state[indices]
action = buf_action[indices]
r_sum = buf_r_sum[indices]
logprob = buf_logprob[indices]
advantage = buf_advantage[indices]
new_logprob = self.act.compute_logprob(state, action) # it is obj_actor
ratio = (new_logprob - logprob).exp()
obj_surrogate1 = advantage * ratio
obj_surrogate2 = advantage * ratio.clamp(1 - self.ratio_clip, 1 + self.ratio_clip)
obj_surrogate = -torch.min(obj_surrogate1, obj_surrogate2).mean()
obj_entropy = (new_logprob.exp() * new_logprob).mean() # policy entropy
obj_actor = obj_surrogate + obj_entropy * self.lambda_entropy
value = self.cri(state).squeeze(1) # critic network predicts the reward_sum (Q value) of state
obj_critic = self.criterion(value, r_sum)
obj_united = obj_actor + obj_critic / (r_sum.std() + 1e-5)
self.optimizer.zero_grad()
obj_united.backward()
self.optimizer.step()
return self.act.a_std_log.mean().item(), obj_critic.item()
def compute_reward_adv(self, buf_len, buf_reward, buf_mask, buf_value) -> (torch.Tensor, torch.Tensor):
"""compute the excepted discounted episode return
:int buf_len: the length of ReplayBuffer
:torch.Tensor buf_reward: buf_reward.shape==(buf_len, 1)
:torch.Tensor buf_mask: buf_mask.shape ==(buf_len, 1)
:torch.Tensor buf_value: buf_value.shape ==(buf_len, 1)
:return torch.Tensor buf_r_sum: buf_r_sum.shape ==(buf_len, 1)
:return torch.Tensor buf_advantage: buf_advantage.shape ==(buf_len, 1)
"""
buf_r_sum = torch.empty(buf_len, dtype=torch.float32, device=self.device) # reward sum
pre_r_sum = 0 # reward sum of previous step
for i in range(buf_len - 1, -1, -1):
buf_r_sum[i] = buf_reward[i] + buf_mask[i] * pre_r_sum
pre_r_sum = buf_r_sum[i]
buf_advantage = buf_r_sum - (buf_mask * buf_value.squeeze(1))
buf_advantage = (buf_advantage - buf_advantage.mean()) / (buf_advantage.std() + 1e-5)
return buf_r_sum, buf_advantage
def compute_reward_gae(self, buf_len, buf_reward, buf_mask, buf_value) -> (torch.Tensor, torch.Tensor):
"""compute the excepted discounted episode return
:int buf_len: the length of ReplayBuffer
:torch.Tensor buf_reward: buf_reward.shape==(buf_len, 1)
:torch.Tensor buf_mask: buf_mask.shape ==(buf_len, 1)
:torch.Tensor buf_value: buf_value.shape ==(buf_len, 1)
:return torch.Tensor buf_r_sum: buf_r_sum.shape ==(buf_len, 1)
:return torch.Tensor buf_advantage: buf_advantage.shape ==(buf_len, 1)
"""
buf_r_sum = torch.empty(buf_len, dtype=torch.float32, device=self.device) # old policy value
buf_advantage = torch.empty(buf_len, dtype=torch.float32, device=self.device) # advantage value
pre_r_sum = 0 # reward sum of previous step
pre_advantage = 0 # advantage value of previous step
for i in range(buf_len - 1, -1, -1):
buf_r_sum[i] = buf_reward[i] + buf_mask[i] * pre_r_sum
pre_r_sum = buf_r_sum[i]
buf_advantage[i] = buf_reward[i] + buf_mask[i] * pre_advantage - buf_value[i]
pre_advantage = buf_value[i] + buf_advantage[i] * self.lambda_gae_adv
buf_advantage = (buf_advantage - buf_advantage.mean()) / (buf_advantage.std() + 1e-5)
return buf_r_sum, buf_advantage
class AgentPPO(AgentBase):
def __init__(self, net_dim, state_dim, action_dim, learning_rate=1e-4):
super().__init__()
self.clip = 0.25 # ratio.clamp(1 - clip, 1 + clip)
self.lambda_entropy = 0.01 # larger lambda_entropy means more exploration
self.act = ActorPPO(net_dim, state_dim, action_dim).to(self.device)
self.cri = CriticAdv(state_dim, net_dim).to(self.device)
self.criterion = torch.nn.SmoothL1Loss()
self.optimizer = torch.optim.Adam([{
'params': self.act.parameters(),
'lr': learning_rate
}, {
'params': self.cri.parameters(),
'lr': learning_rate
}])
def select_actions(self, states): # states = (state, ...)
states = torch.as_tensor(states,
dtype=torch.float32,
device=self.device)
a_noise, noise = self.act.get__action_noise(states)
return a_noise.detach().cpu().numpy(), noise.detach().cpu().numpy()
def update_buffer(self, env, buffer, max_step, reward_scale, gamma):
buffer.empty_memories__before_explore()
step_counter = 0
target_step = buffer.max_len - max_step
while step_counter < target_step:
state = env.reset()
for _ in range(max_step):
action, noise = self.select_actions((state, ))
action = action[0]
noise = noise[0]
next_state, reward, done, _ = env.step(np.tanh(action))
step_counter += 1
other = (reward * reward_scale, 0.0 if done else gamma,
*action, *noise)
buffer.append_memo(state, other)
if done:
break
state = next_state
return step_counter
def update_policy(self, buffer, _max_step, batch_size, repeat_times=8):
buffer.update__now_len__before_sample()
max_memo = buffer.now_len
with torch.no_grad(): # Trajectory using reverse reward
buf_reward, buf_mask, buf_action, buf_noise, buf_state = buffer.sample_for_ppo(
)
bs = 2**10 # set a smaller 'bs: batch size' when out of GPU memory.
buf_value = torch.cat([
self.cri(buf_state[i:i + bs])
for i in range(0, buf_state.size(0), bs)
],
dim=0)
buf_log_prob = -(buf_noise.pow(2).__mul__(0.5) +
self.act.a_std_log + self.act.sqrt_2pi_log).sum(1)
buf_r_sum = torch.empty(max_memo,
dtype=torch.float32,
device=self.device) # reward sum
pre_r_sum = 0 # reward sum of previous step
for i in range(max_memo - 1, -1, -1):
buf_r_sum[i] = buf_reward[i] + buf_mask[i] * pre_r_sum
pre_r_sum = buf_r_sum[i]
buf_advantage = buf_r_sum - (buf_mask * buf_value).squeeze(1)
buf_advantage = buf_advantage / (buf_advantage.std() + 1e-5)
del buf_reward, buf_mask, buf_noise
obj_actor = obj_critic = None
for _ in range(
int(repeat_times * max_memo /
batch_size)): # PPO: Surrogate objective of Trust Region
indices = torch.randint(max_memo,
size=(batch_size, ),
requires_grad=False,
device=self.device)
state = buf_state[indices]
action = buf_action[indices]
r_sum = buf_r_sum[indices]
log_prob = buf_log_prob[indices]
advantage = buf_advantage[indices]
new_log_prob = self.act.compute__log_prob(
state, action) # it is obj_actor
ratio = (new_log_prob - log_prob).exp()
obj_surrogate1 = advantage * ratio
obj_surrogate2 = advantage * ratio.clamp(1 - self.clip,
1 + self.clip)
obj_surrogate = -torch.min(obj_surrogate1, obj_surrogate2).mean()
obj_entropy = (new_log_prob.exp() *
new_log_prob).mean() # policy entropy
obj_actor = obj_surrogate + obj_entropy * self.lambda_entropy
value = self.cri(state).squeeze(
1) # critic network predicts the reward_sum (Q value) of state
obj_critic = self.criterion(value, r_sum)
obj_united = obj_actor + obj_critic / (r_sum.std() + 1e-5)
self.optimizer.zero_grad()
obj_united.backward()
self.optimizer.step()
return obj_actor.item(), obj_critic.item()