259 lines
12 KiB
Python
259 lines
12 KiB
Python
import os
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import pickle
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os.environ['OMP_WAIT_POLICY'] = 'PASSIVE' # 确保在pytorch前设置
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from copy import deepcopy
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import numpy as np
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import torch
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import torch.nn.functional as F
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from models.env import WgzGym
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from models.net import ActorPPO, CriticAdv
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from models.tools import get_episode_return
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def smooth_rewards(rewards, window=10):
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rewards = rewards.unsqueeze(0).unsqueeze(0) # 将 rewards 转为 [1, 1, len] 的形状以适应 conv1d
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kernel = torch.ones(1, 1, window, device=rewards.device) / window # 创建一个均匀的滑动平均核
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smoothed_rewards = F.conv1d(rewards, kernel, padding='valid') # 滑动平均
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smoothed_rewards = smoothed_rewards.squeeze(0).squeeze(0) # 去掉多余的维度
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# 保持与原始奖励序列相同的长度，将前 window-1 个奖励保持不变
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return torch.cat((rewards[0, 0, :window - 1], smoothed_rewards))
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def update_buffer(_trajectory):
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_trajectory = list(map(list, zip(*_trajectory))) # 2D-list transpose, here cut the trajectory into 5 parts
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ten_state = torch.as_tensor(_trajectory[0]) # tensor state here
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ten_reward = torch.as_tensor(_trajectory[1], dtype=torch.float32)
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# _trajectory[2] = done, 将 done 替换为掩码，节省内存
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ten_mask = (1.0 - torch.as_tensor(_trajectory[2], dtype=torch.float32)) * gamma
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ten_action = torch.as_tensor(_trajectory[3])
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ten_noise = torch.as_tensor(_trajectory[4], dtype=torch.float32)
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buffer[:] = (ten_state, ten_action, ten_noise, ten_reward, ten_mask) # list store tensors
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_steps = ten_reward.shape[0] # steps collected in all trajectories
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_r_exp = ten_reward.mean() # the mean reward
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return _steps, _r_exp
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class AgentPPO:
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def __init__(self):
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super().__init__()
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self.state = None
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self.device = None
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self.action_dim = None
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self.criterion = torch.nn.SmoothL1Loss()
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self.cri = self.cri_target = self.if_use_cri_target = self.cri_optim = self.ClassCri = None
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self.act = self.act_target = self.if_use_act_target = self.act_optim = self.ClassAct = None
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'''init modify'''
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self.ClassCri = CriticAdv
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self.ClassAct = ActorPPO
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self.ratio_clip = 0.2
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self.lambda_entropy = 0.02 # be 0.01~0.05
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self.lambda_gae_adv = 0.98 # be 0.95~0.99
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self.get_reward_sum = None
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self.trajectory_list = None
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def init(self, net_dim, state_dim, action_dim, learning_rate=1e-4, gpu_id=0):
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self.device = torch.device(f"cuda:{gpu_id}" if (torch.cuda.is_available() and (gpu_id >= 0)) else "cpu")
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self.trajectory_list = list()
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self.get_reward_sum = self.get_reward_sum_gae
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self.cri = self.ClassCri(net_dim, state_dim, action_dim).to(self.device)
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self.act = self.ClassAct(net_dim, state_dim, action_dim).to(self.device) if self.ClassAct else self.cri
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self.cri_target = deepcopy(self.cri) if self.if_use_cri_target else self.cri
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self.act_target = deepcopy(self.act) if self.if_use_act_target else self.act
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self.cri_optim = torch.optim.Adam(self.cri.parameters(), learning_rate)
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self.act_optim = torch.optim.Adam(self.act.parameters(), learning_rate) if self.ClassAct else self.cri
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def select_action(self, state):
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states = torch.as_tensor((state,), dtype=torch.float32, device=self.device)
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actions, noises = self.act.get_action(states)
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return actions[0].detach().cpu().numpy(), noises[0].detach().cpu().numpy()
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def explore_env(self, env, target_step):
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state = self.state
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trajectory_temp = list()
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last_done = 0
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for i in range(target_step):
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action, noise = self.select_action(state)
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state, next_state, reward, done = env.step(np.tanh(action))
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trajectory_temp.append((state, reward, done, action, noise))
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if done:
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state = env.reset()
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last_done = i
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else:
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state = next_state
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self.state = state
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'''splice list'''
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# store 0 trajectory information to list
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trajectory_list = self.trajectory_list + trajectory_temp[:last_done + 1]
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self.trajectory_list = trajectory_temp[last_done:]
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return trajectory_list
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def update_net(self, buffer, batch_size, repeat_times, soft_update_tau):
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"""put data extract and update network together"""
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with torch.no_grad():
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buf_len = buffer[0].shape[0]
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# decompose buffer data
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buf_state, buf_action, buf_noise, buf_reward, buf_mask = [ten.to(self.device) for ten in buffer]
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'''get buf_r_sum, buf_logprob'''
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bs = batch_size
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buf_value = [self.cri_target(buf_state[i:i + bs]) for i in range(0, buf_len, bs)]
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buf_value = torch.cat(buf_value, dim=0)
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buf_logprob = self.act.get_old_logprob(buf_action, buf_noise)
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buf_r_sum, buf_advantage = self.get_reward_sum(buf_len, buf_reward, buf_mask, buf_value) # detach()
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# normalize advantage
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buf_advantage = (buf_advantage - buf_advantage.mean()) / (buf_advantage.std() + 1e-5)
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buf_advantage = smooth_rewards(buf_advantage, window=10)
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del buf_noise, buffer[:]
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'''PPO: Surrogate objective of Trust Region'''
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obj_critic = obj_actor = None
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for _ in range(int(buf_len / batch_size * repeat_times)):
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indices = torch.randint(buf_len, size=(batch_size,), requires_grad=False, device=self.device)
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state = buf_state[indices]
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action = buf_action[indices]
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r_sum = buf_r_sum[indices]
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logprob = buf_logprob[indices]
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advantage = buf_advantage[indices]
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new_logprob, obj_entropy = self.act.get_logprob_entropy(state, action) # it is obj_actor
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ratio = (new_logprob - logprob.detach()).exp()
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surrogate1 = advantage * ratio
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surrogate2 = advantage * ratio.clamp(1 - self.ratio_clip, 1 + self.ratio_clip)
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obj_surrogate = -torch.min(surrogate1, surrogate2).mean()
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obj_actor = obj_surrogate + obj_entropy * self.lambda_entropy
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self.optim_update(self.act_optim, obj_actor) # update actor
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value = self.cri(state).squeeze(1) # critic network predicts the reward_sum (Q value) of state
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# use smoothloss L1 to evaluate the value loss
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# obj_critic = self.criterion(value, r_sum) / (r_sum.std() + 1e-6)
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obj_critic = self.criterion(value, r_sum)
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self.optim_update(self.cri_optim, obj_critic) # calculate and update the back propogation of value loss
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# choose to use soft update
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self.soft_update(self.cri_target, self.cri, soft_update_tau) if self.cri_target is not self.cri else None
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a_std_log = getattr(self.act, 'a_std_log', torch.zeros(1))
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return obj_critic.item(), obj_actor.item(), a_std_log.mean().item() # logging_tuple
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def get_reward_sum_gae(self, buf_len, ten_reward, ten_mask, ten_value) -> (torch.Tensor, torch.Tensor):
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buf_r_sum = torch.empty(buf_len, dtype=torch.float32, device=self.device) # old policy value
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buf_advantage = torch.empty(buf_len, dtype=torch.float32, device=self.device) # advantage value
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pre_r_sum = 0.0
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pre_advantage = 0.0 # advantage value of previous step
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for i in range(buf_len - 1, -1, -1):
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buf_r_sum[i] = ten_reward[i] + ten_mask[i] * pre_r_sum
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pre_r_sum = buf_r_sum[i]
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buf_advantage[i] = ten_reward[i] + ten_mask[i] * (pre_advantage - ten_value[i])
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pre_advantage = ten_value[i] + buf_advantage[i] * self.lambda_gae_adv
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return buf_r_sum, buf_advantage
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@staticmethod
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def optim_update(optimizer, objective):
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optimizer.zero_grad()
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objective.backward()
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optimizer.step()
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@staticmethod
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def soft_update(target_net, current_net, tau):
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for tar, cur in zip(target_net.parameters(), current_net.parameters()):
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tar.data.copy_(cur.data.__mul__(tau) + tar.data.__mul__(1.0 - tau))
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class Arguments:
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def __init__(self, agent=None, env=None):
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self.agent = agent
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self.env = env
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self.visible_gpu = '0' # os.environ['CUDA_VISIBLE_DEVICES'] = '0, 2,'
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self.num_threads = 32 # cpu_num for evaluate model
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'''Arguments for training'''
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self.num_episode = 1000
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self.gamma = 0.995 # discount factor of reward
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self.learning_rate = 1e-4 # 1e-4 2 ** -14 2e-4
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self.soft_update_tau = 2 ** -8 # 1e-3 2 ** -8
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self.net_dim = 256 # the network width
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self.batch_size = 4096 # num of transitions sampled from replay buffer
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self.repeat_times = 2 ** 5 # collect target_step, then update network
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self.target_step = 4096 # repeatedly update network to keep critic's loss small
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self.max_memo = self.target_step # capacity of replay buffer
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'''Arguments for evaluate'''
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self.random_seed = 1234
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# self.random_seed_list = [1234, 2234, 3234, 4234, 5234]
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self.random_seed_list = [1234]
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self.train = True
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self.save_network = True
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def init_before_training(self):
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np.random.seed(self.random_seed)
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torch.manual_seed(self.random_seed)
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torch.set_num_threads(self.num_threads)
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torch.set_default_dtype(torch.float32)
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os.environ['CUDA_VISIBLE_DEVICES'] = str(self.visible_gpu)
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if __name__ == '__main__':
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args = Arguments()
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reward_record = {'episode': [], 'steps': [], 'mean_episode_reward': [], 'unbalance': []}
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loss_record = {'episode': [], 'steps': [], 'critic_loss': [], 'actor_loss': [], 'entropy_loss': []}
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args.visible_gpu = '0'
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for seed in args.random_seed_list:
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args.random_seed = seed
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args.agent = AgentPPO()
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args.agent.cri_target = True
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args.env = WgzGym()
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args.init_before_training()
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'''init agent and environment'''
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agent = args.agent
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env = args.env
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agent.init(args.net_dim, env.state_space.shape[0], env.action_space.shape[0], args.learning_rate)
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gamma = args.gamma
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batch_size = args.batch_size
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target_step = args.target_step
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repeat_times = args.repeat_times
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soft_update_tau = args.soft_update_tau
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num_episode = args.num_episode
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agent.state = env.reset()
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'''init buffer'''
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buffer = list()
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'''init training params'''
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# args.train = False
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# args.save_network = False
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if args.train:
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for i_episode in range(num_episode):
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with torch.no_grad():
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trajectory_list = agent.explore_env(env, target_step)
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_steps, _r_exp = update_buffer(trajectory_list)
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critic_loss, actor_loss, entropy_loss = agent.update_net(buffer, batch_size, repeat_times,
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soft_update_tau)
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loss_record['critic_loss'].append(critic_loss)
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loss_record['actor_loss'].append(actor_loss)
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loss_record['entropy_loss'].append(entropy_loss)
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with torch.no_grad():
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episode_reward, episode_unbalance = get_episode_return(env, agent.act, agent.device)
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reward_record['mean_episode_reward'].append(episode_reward)
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reward_record['unbalance'].append(episode_unbalance)
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print(f'epsiode: {i_episode}, reward: {episode_reward}, unbalance: {episode_unbalance}')
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act_save_path = './data/actor.pth'
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loss_record_path = './data/loss.pkl'
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reward_record_path = './data/reward.pkl'
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if args.save_network:
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with open(loss_record_path, 'wb') as tf:
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pickle.dump(loss_record, tf)
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with open(reward_record_path, 'wb') as tf:
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pickle.dump(reward_record, tf)
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torch.save(agent.act.state_dict(), act_save_path)
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print('actor params have been saved')
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