ML-Agents与自己的强化学习算法通讯—

这篇具有很好参考价值的文章主要介绍了ML-Agents与自己的强化学习算法通讯——PPO篇。希望对大家有所帮助。如果存在错误或未考虑完全的地方，请大家不吝赐教，您也可以点击"举报违法"按钮提交疑问。

在上一篇文章ML-Agents与python的Low Level API通信中，我简要介绍了Python与Unity端的ML-Agents插件的通讯代码，如何正确运行一个能够进行强化学习训练的Unity环境，并获取到响应的信息，接下来将介绍如何利用自己的强化学习算法进行训练。

介绍

这里我利用了强化学习库PARL来训练ML-Agents的3DBall，使用的是PPO算法。
关于PPO的具体代码细节可以参考我以前的文章强化学习PPO代码讲解，这里不再讲述PPO的代码细节（之所以选择PARL，是因为感觉其代码通俗易懂）
PARL主要将代码分为了几个部分，首先是Model脚本，主要用来编写Actor，Critic等神经网络模型。然后是Algorithm脚本，主要编写具体的算法细节，主要有sample，predict，learn函数。还有storage脚本，主要用来存放经验池（reply buffer）。还有Config脚本，存放训练使用的超参数。Agent脚本，用来对Algorithm脚本进行进一步封装，是与环境交互的接口。最后才是训练入口脚本，调用agent脚本和环境进行交互。

主要源码分析

对于PPO算法，我们可以将其分为两个阶段。第一个是收集数据阶段，一个是训练模型阶段。
和SAC，DDPG等off-policy算法类型，PPO也有经验池，但是PPO是on-policy算法，所以收集数据和训练不能同时进行，每一次训练过后，我们都需要把旧的数据丢弃，重新用训练后的模型采集训练数据。
因此，大致流程是这样的：

所有智能体采集n个step的数据，存放到经验池中。
采集完成后，计算各个step的advantage，logprob等数据，同样存放起来。
利用经验池的数据进行m次PPO的更新
清空经验池数据，重新采样

from mlagents_envs.environment import UnityEnvironment
import numpy as np
from mlagents_envs.environment import ActionTuple
import argparse
import numpy as np
from parl.utils import logger, summary

from storage import RolloutStorage
from parl.algorithms import PPO
from agent import PPOAgent
from genenal_model import GenenalModel_Continuous_Divide
from genenal_config import genenal_config_continuous
from mlagents_envs.side_channel.engine_configuration_channel import EngineConfigurationChannel

# 创建环境
channel = EngineConfigurationChannel()
env = UnityEnvironment(file_name="UnityEnvironment", seed=1, side_channels=[channel])
channel.set_configuration_parameters(time_scale = 3.0)
env.reset()
# 获取环境信息
behavior_names = list(env.behavior_specs.keys())
behavior_value = list(env.behavior_specs.values())
for i in range(len(behavior_names)):
    print(behavior_names[i])
    print("obs:",behavior_value[i].observation_specs[0], "   act:", behavior_value[0].action_spec)
discrete_actions = None
total_steps = 0
stepsNum = 0
obs_space = behavior_value[i].observation_specs[0]
act_space = behavior_value[i].action_spec.continuous_size
# 建立Actor Critic模型
model = GenenalModel_Continuous_Divide(obs_space, act_space, [256,128], [256,128])
config = genenal_config_continuous
config['batch_size'] = int(config['env_num'] * config['step_nums'])
config['num_updates'] = int(
    config['train_total_steps'] // config['batch_size'])
# 建立PPO算法
ppo = PPO(
        model,
        clip_param=config['clip_param'],
        entropy_coef=config['entropy_coef'],
        initial_lr=config['initial_lr'],
        continuous_action=config['continuous_action'])
agent = PPOAgent(ppo, config)
# 建立经验池
rollout = RolloutStorage(config['step_nums'], config['env_num'], obs_space,
                         act_space)

DecisionSteps, TerminalSteps = env.get_steps(behavior_names[0])
obs = DecisionSteps.obs[0]
agentsNum = len(DecisionSteps)
done = np.zeros(agentsNum, dtype='float32')
total_reward = np.zeros(agentsNum, dtype='float32')
this_action = np.zeros((agentsNum, act_space), dtype='float32')
next_obs = np.zeros((agentsNum, obs_space.shape[0]), dtype='float32')
for update in range(1, config['num_updates'] + 1):
    # 数据收集
    for step in range(0, config['step_nums']):
        value, action, logprob, _ = agent.sample(obs)
        agentsNumNow = len(DecisionSteps)
        if agentsNumNow == 0:
            action = np.random.rand(0, 2)
        else:
            action = action.reshape(agentsNumNow, act_space)
            this_action = action
        actions = ActionTuple(action, discrete_actions)
        env.set_actions(behavior_names[0], actions)
        env.step()
        DecisionSteps, TerminalSteps = env.get_steps(behavior_names[0])
        next_obs_Decision = DecisionSteps.obs[0]
        next_obs_Terminal = TerminalSteps.obs[0]
        if(len(next_obs_Terminal) != 0):
            next_obs = np.zeros((agentsNum, obs_space.shape[-1]))
            rewards = np.zeros(agentsNum, dtype=float)
            next_done = np.zeros(agentsNum, dtype=bool)
            j = 0
            for i in TerminalSteps.agent_id:
                next_obs[i] = next_obs_Terminal[j]
                rewards[i] = TerminalSteps.reward[j]
                next_done[i] = True
                j += 1
            rollout.append(obs, this_action, logprob, rewards, done, value.flatten())
            obs, done = next_obs, next_done
            total_reward += rewards

        if(len(next_obs_Decision) != 0):
            step += 1
            next_obs = next_obs_Decision
            rewards = DecisionSteps.reward
            next_done = np.zeros(agentsNum, dtype=bool)

            rollout.append(obs, this_action, logprob, rewards, done, value.flatten())
            obs, done = next_obs, next_done
            total_reward += rewards

        total_steps += 1
        stepsNum += 1
        if(stepsNum % 200 == 199):
            arv_reward = total_reward / 200
            print("total_steps:{0}".format(total_steps))
            print("arv_reward:", arv_reward)
            stepsNum = 0
            total_reward = 0
    # PPO训练模型
    value = agent.value(obs)
    rollout.compute_returns(value, done)    
    value_loss, action_loss, entropy_loss, lr = agent.learn(rollout)


env.close()