421 lines
19 KiB
Python
421 lines
19 KiB
Python
import os
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import pickle
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from copy import deepcopy
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import numpy as np
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import pandas as pd
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import torch
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import torch.nn as nn
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from environment import ESSEnv
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from tools import get_episode_return, test_one_episode, optimization_base_result
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os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE'
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class ActorPPO(nn.Module):
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def __init__(self, mid_dim, state_dim, action_dim, layer_norm=False):
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super().__init__()
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self.layer_norm = layer_norm
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self.net = nn.Sequential(nn.Linear(state_dim, mid_dim), nn.ReLU(),
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nn.Linear(mid_dim, mid_dim), nn.ReLU(),
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nn.Linear(mid_dim, mid_dim), nn.Hardswish(),
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nn.Linear(mid_dim, action_dim))
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# the logarithm (log) of standard deviation (std) of action, it is a trainable parameter
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self.a_logstd = nn.Parameter(torch.zeros((1, action_dim)) - 0.5, requires_grad=True)
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self.sqrt_2pi_log = np.log(np.sqrt(2 * np.pi))
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if self.layer_norm:
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self.apply_layer_norm()
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def apply_layer_norm(self):
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def init_weights(layer):
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if isinstance(layer, nn.Linear):
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nn.init.orthogonal_(layer.weight, 1.0)
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nn.init.constant_(layer.bias, 0.0)
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self.net.apply(init_weights)
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def forward(self, state):
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return self.net(state).tanh() # action.tanh() limit the data output of action
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def get_action(self, state):
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a_avg = self.forward(state) # too big for the action
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a_std = self.a_logstd.exp()
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noise = torch.randn_like(a_avg)
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action = a_avg + noise * a_std
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return action, noise
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def get_logprob_entropy(self, state, action):
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a_avg = self.forward(state)
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a_std = self.a_logstd.exp()
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delta = ((a_avg - action) / a_std).pow(2) * 0.5
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logprob = -(self.a_logstd + self.sqrt_2pi_log + delta).sum(1) # new_logprob
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dist_entropy = (logprob.exp() * logprob).mean() # policy entropy
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return logprob, dist_entropy
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def get_old_logprob(self, _action, noise): # noise = action - a_noise
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delta = noise.pow(2) * 0.5
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return -(self.a_logstd + self.sqrt_2pi_log + delta).sum(1) # old_logprob
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class CriticAdv(nn.Module):
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def __init__(self, mid_dim, state_dim, _action_dim, layer_norm=False):
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super().__init__()
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self.layer_norm = layer_norm
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self.net = nn.Sequential(nn.Linear(state_dim, mid_dim), nn.ReLU(),
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nn.Linear(mid_dim, mid_dim), nn.ReLU(),
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nn.Linear(mid_dim, mid_dim), nn.Hardswish(),
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nn.Linear(mid_dim, 1))
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if self.layer_norm:
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self.apply_layer_norm()
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def apply_layer_norm(self):
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def init_weights(layer):
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if isinstance(layer, nn.Linear):
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nn.init.orthogonal_(layer.weight, 1.0)
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nn.init.constant_(layer.bias, 0.0)
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self.net.apply(init_weights)
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def forward(self, state):
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return self.net(state) # Advantage value
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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.get_obj_critic = 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 # ratio.clamp(1 - clip, 1 + clip)
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self.lambda_entropy = 0.02 # could be 0.01~0.05
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self.lambda_gae_adv = 0.98 # could be 0.95~0.99, GAE (Generalized Advantage Estimation. ICLR.2016.)
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self.get_reward_sum = None # self.get_reward_sum_gae if if_use_gae else self.get_reward_sum_raw
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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, if_use_gae=False, gpu_id=0, layer_norm=False):
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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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# choose whether to use gae or not
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self.get_reward_sum = self.get_reward_sum_gae if if_use_gae else self.get_reward_sum_raw
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self.cri = self.ClassCri(net_dim, state_dim, action_dim, layer_norm).to(self.device)
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self.act = self.ClassAct(net_dim, state_dim, action_dim, layer_norm).to(
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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 # sent state to agent and then agent sent state to method
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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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# the step of cut action is finally organized into the environment
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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 = 4096
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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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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 whether 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_raw(self, buf_len, buf_reward, buf_mask, buf_value) -> (torch.Tensor, torch.Tensor):
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buf_r_sum = torch.empty(buf_len, dtype=torch.float32, device=self.device) # reward sum
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pre_r_sum = 0
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for i in range(buf_len - 1, -1, -1):
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buf_r_sum[i] = buf_reward[i] + buf_mask[i] * pre_r_sum
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pre_r_sum = buf_r_sum[i]
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buf_advantage = buf_r_sum - (buf_mask * buf_value[:, 0])
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return buf_r_sum, buf_advantage
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import torch
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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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# Calculate TD residual delta
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if i == buf_len - 1:
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next_value = 0.0
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else:
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next_value = ten_value[i + 1]
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delta = ten_reward[i] + ten_mask[i] * gamma * next_value - ten_value[i]
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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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pre_advantage = delta + ten_mask[i] * gamma * self.lambda_gae_adv * pre_advantage
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buf_advantage[i] = pre_advantage
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return buf_r_sum, buf_advantage
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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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#
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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]) # fix a bug here
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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 # Deep Reinforcement Learning algorithm
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self.env = env # the environment for training
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self.cwd = None # current work directory. None means set automatically
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self.if_remove = False # remove the cwd folder? (True, False, None:ask me)
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self.visible_gpu = '0' # for example: os.environ['CUDA_VISIBLE_DEVICES'] = '0, 2,'
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self.num_threads = 32 # cpu_num for evaluate model, torch.set_num_threads(self.num_threads)
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'''Arguments for training'''
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self.num_episode = 1000 # to control the train episodes for PPO
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self.gamma = 0.995 # discount factor of future rewards
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self.learning_rate = 2 ** -14 # 2e-4
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self.soft_update_tau = 2 ** -8 # 2 ** -8 ~= 5e-3
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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 ** 3 # 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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self.if_gae_or_raw = True # GAE for on-policy sparse reward: Generalized Advantage Estimation.
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'''Arguments for evaluate'''
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self.random_seed = 0 # initialize random seed in self.init_before_training()
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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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self.test_network = True
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self.save_test_data = True
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self.compare_with_gurobi = True
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self.plot_on = True
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def init_before_training(self, if_main):
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if self.cwd is None:
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agent_name = self.agent.__class__.__name__
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self.cwd = f'./{agent_name}'
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if if_main:
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import shutil # remove history according to bool(if_remove)
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if self.if_remove is None:
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self.if_remove = (input(f"| PRESS 'y' to REMOVE: {self.cwd}? ") == 'y')
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elif self.if_remove:
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shutil.rmtree(self.cwd, ignore_errors=True)
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print(f"| Remove cwd: {self.cwd}")
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os.makedirs(self.cwd, exist_ok=True)
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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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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, replace done by mask, save memory
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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] # how many steps are 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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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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agent_name = f'{args.agent.__class__.__name__}'
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args.agent.cri_target = True
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args.env = ESSEnv()
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args.init_before_training(if_main=True)
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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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args.if_gae_or_raw, layer_norm=True)
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cwd = args.cwd
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gamma = args.gamma
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batch_size = args.batch_size # how much data should be used to update net
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target_step = args.target_step # how manysteps of one episode should stop
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repeat_times = args.repeat_times # how many times should update for one batch size data
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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 parameters'''
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# args.train = False
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# args.save_network = False
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# args.test_network = False
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# args.save_test_data = False
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# args.compare_with_gurobi = False
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# args.plot_on = 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 = f'{args.cwd}/actor.pth'
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loss_record_path = f'{args.cwd}/loss_data.pkl'
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reward_record_path = f'{args.cwd}/reward_data.pkl'
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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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if args.save_network:
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torch.save(agent.act.state_dict(), act_save_path)
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print('actor parameters have been saved')
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if args.test_network:
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args.cwd = agent_name
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agent.act.load_state_dict(torch.load(act_save_path))
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print('parameters have been reload and test')
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record = test_one_episode(env, agent.act, agent.device)
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eval_data = pd.DataFrame(record['system_info'])
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eval_data.columns = ['time_step', 'price', 'netload', 'action', 'real_action', 'soc', 'battery', 'gen1', 'gen2',
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'gen3', 'temperature', 'irradiance', 'unbalance', 'operation_cost']
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if args.save_test_data:
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test_data_save_path = f'{args.cwd}/test_data.pkl'
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with open(test_data_save_path, 'wb') as tf:
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pickle.dump(record, tf)
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'''compare with gurobi data and results'''
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if args.compare_with_gurobi:
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month = record['init_info'][0][0]
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day = record['init_info'][0][1]
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initial_soc = record['init_info'][0][3]
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base_result = optimization_base_result(env, month, day, initial_soc)
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if args.plot_on:
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from plotDRL import PlotArgs, make_dir, plot_evaluation_information, plot_optimization_result
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|
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plot_args = PlotArgs()
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|
plot_args.feature_change = 'gae'
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|
args.cwd = agent_name
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|
plot_dir = make_dir(args.cwd, plot_args.feature_change)
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plot_optimization_result(base_result, plot_dir)
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plot_evaluation_information(args.cwd + '/' + 'test_data.pkl', plot_dir)
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'''compare the different cost get from gurobi and PPO'''
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ration = sum(eval_data['operation_cost']) / sum(base_result['step_cost'])
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|
print('rl_cost:', sum(eval_data['operation_cost']))
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print('gurobi_cost:', sum(base_result['step_cost']))
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|
print('ration:', ration)
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