318 lines
14 KiB
Python
318 lines
14 KiB
Python
import numpy as np
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import torch
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import argparse
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import time
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from torch import nn
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from aimbotEnv import Aimbot
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from torch.distributions.normal import Normal
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from torch.distributions.categorical import Categorical
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def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
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nn.init.orthogonal_(layer.weight, std)
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nn.init.constant_(layer.bias, bias_const)
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return layer
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class PPOAgent(nn.Module):
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def __init__(
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self,
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env: Aimbot,
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this_args:argparse.Namespace,
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device: torch.device,
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):
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super(PPOAgent, self).__init__()
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self.device = device
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self.args = this_args
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self.train_agent = self.args.train
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self.target_num = self.args.target_num
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self.unity_observation_shape = env.unity_observation_shape
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self.unity_action_size = env.unity_action_size
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self.state_size = self.unity_observation_shape[0]
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self.agent_num = env.unity_agent_num
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self.target_size = self.args.target_state_size
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self.time_state_size = self.args.time_state_size
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self.gun_state_size = self.args.gun_state_size
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self.my_state_size = self.args.my_state_size
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self.ray_state_size = env.unity_observation_shape[0] - self.args.total_target_size
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self.state_size_without_ray = self.args.total_target_size
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self.head_input_size = (
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env.unity_observation_shape[0] - self.target_size - self.time_state_size - self.gun_state_size
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) # except target state input
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self.unity_discrete_type = env.unity_discrete_type
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self.discrete_size = env.unity_discrete_size
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self.discrete_shape = list(env.unity_discrete_branches)
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self.continuous_size = env.unity_continuous_size
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self.view_network = nn.Sequential(layer_init(nn.Linear(self.ray_state_size, 200)), nn.LeakyReLU())
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self.target_networks = nn.ModuleList(
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[
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nn.Sequential(layer_init(nn.Linear(self.state_size_without_ray, 100)), nn.LeakyReLU())
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for i in range(self.target_num)
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]
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)
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self.middle_networks = nn.ModuleList(
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[
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nn.Sequential(layer_init(nn.Linear(300, 200)), nn.LeakyReLU())
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for i in range(self.target_num)
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]
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)
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self.actor_dis = nn.ModuleList(
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[layer_init(nn.Linear(200, self.discrete_size), std=0.5) for i in range(self.target_num)]
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)
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self.actor_mean = nn.ModuleList(
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[layer_init(nn.Linear(200, self.continuous_size), std=0.5) for i in range(self.target_num)]
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)
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# self.actor_logstd =
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# nn.ModuleList([layer_init(nn.Linear(200, self.continuous_size), std=1) for i in range(targetNum)])
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# self.actor_logstd = nn.Parameter(torch.zeros(1, self.continuous_size))
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self.actor_logstd = nn.ParameterList(
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[nn.Parameter(torch.zeros(1, self.continuous_size)) for i in range(self.target_num)]
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) # nn.Parameter(torch.zeros(1, self.continuous_size))
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self.critic = nn.ModuleList(
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[layer_init(nn.Linear(200, 1), std=1) for i in range(self.target_num)]
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)
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def get_value(self, state: torch.Tensor):
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target = state[:, 0].to(torch.int32) # int
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this_state_num = target.size()[0]
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view_input = state[:, -self.ray_state_size :] # all ray input
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target_input = state[:, : self.state_size_without_ray]
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view_layer = self.view_network(view_input)
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target_layer = torch.stack(
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[self.target_networks[target[i]](target_input[i]) for i in range(this_state_num)]
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)
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middle_input = torch.cat([view_layer, target_layer], dim=1)
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middle_layer = torch.stack(
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[self.middle_networks[target[i]](middle_input[i]) for i in range(this_state_num)]
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)
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criticV = torch.stack(
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[self.critic[target[i]](middle_layer[i]) for i in range(this_state_num)]
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) # self.critic
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return criticV
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def get_actions_value(self, state: torch.Tensor, actions=None):
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target = state[:, 0].to(torch.int32) # int
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this_state_num = target.size()[0]
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view_input = state[:, -self.ray_state_size :] # all ray input
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target_input = state[:, : self.state_size_without_ray]
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view_layer = self.view_network(view_input)
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target_layer = torch.stack(
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[self.target_networks[target[i]](target_input[i]) for i in range(this_state_num)]
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)
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middle_input = torch.cat([view_layer, target_layer], dim=1)
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middle_layer = torch.stack(
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[self.middle_networks[target[i]](middle_input[i]) for i in range(this_state_num)]
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)
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# discrete
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# 递归targets的数量,既agent数来实现根据target不同来选用对应的输出网络计算输出
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dis_logits = torch.stack(
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[self.actor_dis[target[i]](middle_layer[i]) for i in range(this_state_num)]
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)
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split_logits = torch.split(dis_logits, self.discrete_shape, dim=1)
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multi_categoricals = [Categorical(logits=thisLogits) for thisLogits in split_logits]
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# continuous
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actions_mean = torch.stack(
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[self.actor_mean[target[i]](middle_layer[i]) for i in range(this_state_num)]
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) # self.actor_mean(hidden)
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# action_logstd = torch.stack([self.actor_logstd[target[i]](middleLayer[i]) for i in range(thisStateNum)]) # self.actor_logstd(hidden)
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# action_logstd = self.actor_logstd.expand_as(actions_mean) # self.actor_logstd.expand_as(actions_mean)
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action_logstd = torch.stack(
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[torch.squeeze(self.actor_logstd[target[i]], 0) for i in range(this_state_num)]
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)
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# print(action_logstd)
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action_std = torch.exp(action_logstd) # torch.exp(action_logstd)
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con_probs = Normal(actions_mean, action_std)
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# critic
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criticV = torch.stack(
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[self.critic[target[i]](middle_layer[i]) for i in range(this_state_num)]
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) # self.critic
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if actions is None:
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if self.train_agent:
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# select actions base on probability distribution model
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disAct = torch.stack([ctgr.sample() for ctgr in multi_categoricals])
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conAct = con_probs.sample()
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actions = torch.cat([disAct.T, conAct], dim=1)
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else:
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# select actions base on best probability distribution
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# disAct = torch.stack([torch.argmax(logit, dim=1) for logit in split_logits])
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conAct = actions_mean
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disAct = torch.stack([ctgr.sample() for ctgr in multi_categoricals])
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conAct = con_probs.sample()
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actions = torch.cat([disAct.T, conAct], dim=1)
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else:
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disAct = actions[:, 0 : self.unity_discrete_type].T
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conAct = actions[:, self.unity_discrete_type :]
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dis_log_prob = torch.stack(
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[ctgr.log_prob(act) for act, ctgr in zip(disAct, multi_categoricals)]
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)
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dis_entropy = torch.stack([ctgr.entropy() for ctgr in multi_categoricals])
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return (
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actions,
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dis_log_prob.sum(0),
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dis_entropy.sum(0),
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con_probs.log_prob(conAct).sum(1),
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con_probs.entropy().sum(1),
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criticV,
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)
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def train_net(self, this_train_ind:int,ppo_memories,optimizer) -> tuple:
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start_time = time.time()
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# flatten the batch
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b_obs = ppo_memories.obs[this_train_ind].reshape((-1,) + self.unity_observation_shape)
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b_dis_logprobs = ppo_memories.dis_logprobs[this_train_ind].reshape(-1)
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b_con_logprobs = ppo_memories.con_logprobs[this_train_ind].reshape(-1)
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b_actions = ppo_memories.actions[this_train_ind].reshape((-1,) + (self.unity_action_size,))
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b_advantages = ppo_memories.advantages[this_train_ind].reshape(-1)
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b_returns = ppo_memories.returns[this_train_ind].reshape(-1)
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b_values = ppo_memories.values[this_train_ind].reshape(-1)
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b_size = b_obs.size()[0]
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# optimizing the policy and value network
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b_inds = np.arange(b_size)
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for epoch in range(self.args.epochs):
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print("epoch:",epoch,end="")
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# shuffle all datasets
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np.random.shuffle(b_inds)
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for start in range(0, b_size, self.args.minibatchSize):
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print(".",end="")
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end = start + self.args.minibatchSize
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mb_inds = b_inds[start:end]
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if(np.size(mb_inds)<=1):
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break
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mb_advantages = b_advantages[mb_inds]
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# normalize advantages
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if self.args.norm_adv:
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mb_advantages = (mb_advantages - mb_advantages.mean()) / (
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mb_advantages.std() + 1e-8
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)
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(
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_,
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new_dis_logprob,
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dis_entropy,
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new_con_logprob,
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con_entropy,
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newvalue,
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) = self.get_actions_value(b_obs[mb_inds], b_actions[mb_inds])
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# discrete ratio
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dis_logratio = new_dis_logprob - b_dis_logprobs[mb_inds]
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dis_ratio = dis_logratio.exp()
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# continuous ratio
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con_logratio = new_con_logprob - b_con_logprobs[mb_inds]
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con_ratio = con_logratio.exp()
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"""
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# early stop
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with torch.no_grad():
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# calculate approx_kl http://joschu.net/blog/kl-approx.html
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old_approx_kl = (-logratio).mean()
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approx_kl = ((ratio - 1) - logratio).mean()
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clipfracs += [((ratio - 1.0).abs() > args.clip_coef).float().mean().item()]
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"""
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# discrete Policy loss
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dis_pg_loss_orig = -mb_advantages * dis_ratio
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dis_pg_loss_clip = -mb_advantages * torch.clamp(
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dis_ratio, 1 - self.args.clip_coef, 1 + self.args.clip_coef
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)
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dis_pg_loss = torch.max(dis_pg_loss_orig, dis_pg_loss_clip).mean()
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# continuous Policy loss
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con_pg_loss_orig = -mb_advantages * con_ratio
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con_pg_loss_clip = -mb_advantages * torch.clamp(
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con_ratio, 1 - self.args.clip_coef, 1 + self.args.clip_coef
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)
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con_pg_loss = torch.max(con_pg_loss_orig, con_pg_loss_clip).mean()
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# Value loss
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newvalue = newvalue.view(-1)
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if self.args.clip_vloss:
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v_loss_unclipped = (newvalue - b_returns[mb_inds]) ** 2
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v_clipped = b_values[mb_inds] + torch.clamp(
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newvalue - b_values[mb_inds],
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-self.args.clip_coef,
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self.args.clip_coef,
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)
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v_loss_clipped = (v_clipped - b_returns[mb_inds]) ** 2
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v_loss_max = torch.max(v_loss_unclipped, v_loss_clipped)
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v_loss = 0.5 * v_loss_max.mean()
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else:
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v_loss = 0.5 * ((newvalue - b_returns[mb_inds]) ** 2).mean()
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# total loss
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entropy_loss = dis_entropy.mean() + con_entropy.mean()
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loss = (
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dis_pg_loss * self.args.policy_coef[this_train_ind]
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+ con_pg_loss * self.args.policy_coef[this_train_ind]
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+ entropy_loss * self.args.entropy_coef[this_train_ind]
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+ v_loss * self.args.critic_coef[this_train_ind]
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)*self.args.loss_coef[this_train_ind]
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if(torch.isnan(loss).any()):
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print("LOSS Include NAN!!!")
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if(torch.isnan(dis_pg_loss.any())):
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print("dis_pg_loss include nan")
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if(torch.isnan(con_pg_loss.any())):
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print("con_pg_loss include nan")
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if(torch.isnan(entropy_loss.any())):
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print("entropy_loss include nan")
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if(torch.isnan(v_loss.any())):
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print("v_loss include nan")
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raise
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optimizer.zero_grad()
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loss.backward()
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# Clips gradient norm of an iterable of parameters.
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nn.utils.clip_grad_norm_(self.parameters(), self.args.max_grad_norm)
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optimizer.step()
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"""
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if args.target_kl is not None:
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if approx_kl > args.target_kl:
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break
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"""
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return (v_loss,dis_pg_loss,con_pg_loss,loss,entropy_loss)
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def gae(
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self,
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rewards: torch.Tensor,
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dones: torch.Tensor,
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values: torch.tensor,
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next_obs: torch.tensor,
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next_done: torch.Tensor,
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) -> tuple:
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# GAE
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with torch.no_grad():
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next_value = self.get_value(next_obs).reshape(1, -1)
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data_size = rewards.size()[0]
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if self.args.gae:
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advantages = torch.zeros_like(rewards).to(self.device)
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last_gae_lam = 0
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for t in reversed(range(data_size)):
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if t == data_size - 1:
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nextnonterminal = 1.0 - next_done
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next_values = next_value
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else:
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nextnonterminal = 1.0 - dones[t + 1]
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next_values = values[t + 1]
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delta = rewards[t] + self.args.gamma * next_values * nextnonterminal - values[t]
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advantages[t] = last_gae_lam = (
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delta + self.args.gamma * self.args.gaeLambda * nextnonterminal * last_gae_lam
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)
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returns = advantages + values
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else:
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returns = torch.zeros_like(rewards).to(self.device)
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for t in reversed(range(data_size)):
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if t == data_size - 1:
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nextnonterminal = 1.0 - next_done
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next_return = next_value
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else:
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nextnonterminal = 1.0 - dones[t + 1]
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next_return = returns[t + 1]
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returns[t] = rewards[t] + self.args.gamma * nextnonterminal * next_return
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advantages = returns - values
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return advantages, returns |