import numpy as np
import torch
import argparse
import time
from torch import nn
from aimbotEnv import Aimbot
from torch.distributions.normal import Normal
from torch.distributions.categorical import Categorical
def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
nn.init.orthogonal_(layer.weight, std)
nn.init.constant_(layer.bias, bias_const)
return layer
class PPOAgent(nn.Module):
def __init__(
self,
env: Aimbot,
this_args: argparse.Namespace,
device: torch.device,
):
super(PPOAgent, self).__init__()
self.device = device
self.args = this_args
self.train_agent = self.args.train
self.target_num = self.args.target_num
self.unity_observation_shape = env.unity_observation_shape
self.unity_action_size = env.unity_action_size
self.state_size = self.unity_observation_shape[0]
self.agent_num = env.unity_agent_num
self.target_size = self.args.target_state_size
self.time_state_size = self.args.time_state_size
self.gun_state_size = self.args.gun_state_size
self.my_state_size = self.args.my_state_size
self.ray_state_size = env.unity_observation_shape[0] - self.args.total_target_size
self.state_size_without_ray = self.args.total_target_size
self.head_input_size = (
env.unity_observation_shape[0] - self.target_size - self.time_state_size - self.gun_state_size
) # except target state input
self.unity_discrete_type = env.unity_discrete_type
self.discrete_size = env.unity_discrete_size
self.discrete_shape = list(env.unity_discrete_branches)
self.continuous_size = env.unity_continuous_size
self.hidden_networks = nn.ModuleList(
[
nn.Sequential(
layer_init(nn.Linear(self.state_size, 128)),
nn.LeakyReLU(),
layer_init(nn.Linear(128, 64)),
nn.LeakyReLU(),
)
for i in range(self.target_num)
]
)
self.actor_dis = nn.ModuleList(
[layer_init(nn.Linear(64, self.discrete_size), std=0.5) for i in range(self.target_num)]
)
self.actor_mean = nn.ModuleList(
[layer_init(nn.Linear(64, self.continuous_size), std=0.5) for i in range(self.target_num)]
)
self.actor_logstd = nn.ParameterList(
[nn.Parameter(torch.zeros(1, self.continuous_size)) for i in range(self.target_num)]
)
self.critic = nn.ModuleList(
[layer_init(nn.Linear(64, 1), std=1) for i in range(self.target_num)]
)
def get_value(self, state: torch.Tensor):
# get critic value
# state.size()[0] is batch_size
target = state[:, 0].to(torch.int32) # int
hidden_output = torch.stack(
[self.hidden_networks[target[i]](state[i]) for i in range(state.size()[0])]
)
criticV = torch.stack(
[self.critic[target[i]](hidden_output[i]) for i in range(state.size()[0])]
)
return criticV
def get_actions_value(self, state: torch.Tensor, actions=None):
# get actions and value
target = state[:, 0].to(torch.int32) # int
hidden_output = torch.stack(
[self.hidden_networks[target[i]](state[i]) for i in range(target.size()[0])]
)
# discrete
# 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
dis_logits = torch.stack(
[self.actor_dis[target[i]](hidden_output[i]) for i in range(target.size()[0])]
)
split_logits = torch.split(dis_logits, self.discrete_shape, dim=1)
multi_categoricals = [Categorical(logits=thisLogits) for thisLogits in split_logits]
# continuous
actions_mean = torch.stack(
[self.actor_mean[target[i]](hidden_output[i]) for i in range(target.size()[0])]
) # self.actor_mean(hidden)
action_logstd = torch.stack(
[torch.squeeze(self.actor_logstd[target[i]], 0) for i in range(target.size()[0])]
)
# print(action_logstd)
action_std = torch.exp(action_logstd) # torch.exp(action_logstd)
con_probs = Normal(actions_mean, action_std)
# critic
criticV = torch.stack(
[self.critic[target[i]](hidden_output[i]) for i in range(target.size()[0])]
) # self.critic
if actions is None:
if self.train_agent:
# select actions base on probability distribution model
dis_act = torch.stack([ctgr.sample() for ctgr in multi_categoricals])
con_act = con_probs.sample()
actions = torch.cat([dis_act.T, con_act], dim=1)
else:
# select actions base on best probability distribution
dis_act = torch.stack([torch.argmax(logit, dim=1) for logit in split_logits])
con_act = actions_mean
actions = torch.cat([dis_act.T, con_act], dim=1)
else:
dis_act = actions[:, 0: self.unity_discrete_type].T
con_act = actions[:, self.unity_discrete_type:]
dis_log_prob = torch.stack(
[ctgr.log_prob(act) for act, ctgr in zip(dis_act, multi_categoricals)]
)
dis_entropy = torch.stack([ctgr.entropy() for ctgr in multi_categoricals])
return (
actions,
dis_log_prob.sum(0),
dis_entropy.sum(0),
con_probs.log_prob(con_act).sum(1),
con_probs.entropy().sum(1),
criticV,
)
def train_net(self, this_train_ind: int, ppo_memories, optimizer) -> tuple:
start_time = time.time()
# flatten the batch
b_obs = ppo_memories.obs[this_train_ind].reshape((-1,) + self.unity_observation_shape)
b_dis_logprobs = ppo_memories.dis_logprobs[this_train_ind].reshape(-1)
b_con_logprobs = ppo_memories.con_logprobs[this_train_ind].reshape(-1)
b_actions = ppo_memories.actions[this_train_ind].reshape((-1,) + (self.unity_action_size,))
b_advantages = ppo_memories.advantages[this_train_ind].reshape(-1)
b_returns = ppo_memories.returns[this_train_ind].reshape(-1)
b_values = ppo_memories.values[this_train_ind].reshape(-1)
b_size = b_obs.size()[0]
# optimizing the policy and value network
b_index = np.arange(b_size)
for epoch in range(self.args.epochs):
print("epoch:", epoch, end="")
# shuffle all datasets
np.random.shuffle(b_index)
for start in range(0, b_size, self.args.minibatchSize):
print(".", end="")
end = start + self.args.minibatchSize
mb_index = b_index[start:end]
if np.size(mb_index) <= 1:
break
mb_advantages = b_advantages[mb_index]
# normalize advantages
if self.args.norm_adv:
mb_advantages = (mb_advantages - mb_advantages.mean()) / (
mb_advantages.std() + 1e-8
)
(
_,
new_dis_logprob,
dis_entropy,
new_con_logprob,
con_entropy,
new_value,
) = self.get_actions_value(b_obs[mb_index], b_actions[mb_index])
# discrete ratio
dis_log_ratio = new_dis_logprob - b_dis_logprobs[mb_index]
dis_ratio = dis_log_ratio.exp()
# continuous ratio
con_log_ratio = new_con_logprob - b_con_logprobs[mb_index]
con_ratio = con_log_ratio.exp()
"""
# early stop
with torch.no_grad():
# calculate approx_kl http://joschu.net/blog/kl-approx.html
old_approx_kl = (-logratio).mean()
approx_kl = ((ratio - 1) - logratio).mean()
clipfracs += [((ratio - 1.0).abs() > args.clip_coef).float().mean().item()]
"""
# discrete Policy loss
dis_pg_loss_orig = -mb_advantages * dis_ratio
dis_pg_loss_clip = -mb_advantages * torch.clamp(
dis_ratio, 1 - self.args.clip_coef, 1 + self.args.clip_coef
)
dis_pg_loss = torch.max(dis_pg_loss_orig, dis_pg_loss_clip).mean()
# continuous Policy loss
con_pg_loss_orig = -mb_advantages * con_ratio
con_pg_loss_clip = -mb_advantages * torch.clamp(
con_ratio, 1 - self.args.clip_coef, 1 + self.args.clip_coef
)
con_pg_loss = torch.max(con_pg_loss_orig, con_pg_loss_clip).mean()
# Value loss
new_value = new_value.view(-1)
if self.args.clip_vloss:
v_loss_unclipped = (new_value - b_returns[mb_index]) ** 2
v_clipped = b_values[mb_index] + torch.clamp(
new_value - b_values[mb_index],
-self.args.clip_coef,
self.args.clip_coef,
)
v_loss_clipped = (v_clipped - b_returns[mb_index]) ** 2
v_loss_max = torch.max(v_loss_unclipped, v_loss_clipped)
v_loss = 0.5 * v_loss_max.mean()
else:
v_loss = 0.5 * ((new_value - b_returns[mb_index]) ** 2).mean()
# total loss
entropy_loss = dis_entropy.mean() + con_entropy.mean()
loss = (
dis_pg_loss * self.args.policy_coef[this_train_ind]
+ con_pg_loss * self.args.policy_coef[this_train_ind]
+ entropy_loss * self.args.entropy_coef[this_train_ind]
+ v_loss * self.args.critic_coef[this_train_ind]
) * self.args.loss_coef[this_train_ind]
if torch.isnan(loss).any():
print("LOSS Include NAN!!!")
if torch.isnan(dis_pg_loss.any()):
print("dis_pg_loss include nan")
if torch.isnan(con_pg_loss.any()):
print("con_pg_loss include nan")
if torch.isnan(entropy_loss.any()):
print("entropy_loss include nan")
if torch.isnan(v_loss.any()):
print("v_loss include nan")
raise
optimizer.zero_grad()
loss.backward()
# Clips gradient norm of an iterable of parameters.
nn.utils.clip_grad_norm_(self.parameters(), self.args.max_grad_norm)
optimizer.step()
"""
if args.target_kl is not None:
if approx_kl > args.target_kl:
break
"""
return v_loss, dis_pg_loss, con_pg_loss, loss, entropy_loss
def gae(
self,
rewards: torch.Tensor,
dones: torch.Tensor,
values: torch.Tensor,
next_obs: torch.Tensor,
next_done: torch.Tensor,
) -> tuple:
# GAE
with torch.no_grad():
next_value = self.get_value(next_obs).reshape(1, -1)
data_size = rewards.size()[0]
if self.args.gae:
advantages = torch.zeros_like(rewards).to(self.device)
last_gae_lam = 0
for t in reversed(range(data_size)):
if t == data_size - 1:
next_non_terminal = 1.0 - next_done
next_values = next_value
else:
next_non_terminal = 1.0 - dones[t + 1]
next_values = values[t + 1]
delta = rewards[t] + self.args.gamma * next_values * next_non_terminal - values[t]
advantages[t] = last_gae_lam = (
delta + self.args.gamma * self.args.gaeLambda * next_non_terminal * last_gae_lam
)
returns = advantages + values
else:
returns = torch.zeros_like(rewards).to(self.device)
for t in reversed(range(data_size)):
if t == data_size - 1:
next_non_terminal = 1.0 - next_done
next_return = next_value
else:
next_non_terminal = 1.0 - dones[t + 1]
next_return = returns[t + 1]
returns[t] = rewards[t] + self.args.gamma * next_non_terminal * next_return
advantages = returns - values
return advantages, returns