Matsuo, H. Sakaguchi, ppohDEM: computational performance for open source code of the discrete element method, Comput. Phys. Commun. 185 (2014) 1486-1495.Nishiura D, Matsuo M Y, Sakaguchi H. ppohDEM: Com- putatio
The source code for the blog post The 37 Implementation Details of Proximal Policy Optimization - vwxyzjn/ppo-implementation-details
More detailed settings can be found in our open-source code. We show the complete training dynamics of PPO-max in Figure 9. Figure 9: 10K steps training dynamics of PPO-max. PPO-max ensures long-term stable policy optimization for the model....
eval_policy.py: 用于评估训练好的策略。 graph_code/: 包含自动收集数据和生成图表的代码。 使用方法 创建并激活Python虚拟环境: python -m venv venv source venv/bin/activate pip install -r requirements.txt 从头开始训练: python main.py 测试已训练的模型: python main.py --mode test --actor_model ...
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使用MultipartFile一直提示无法访问org.springframework.core.io.InputStreamSource,上网搜,说是因为没有引入spring.core依赖,在pom文件中添加 仍然提示同样的错误。后面点进去idea 的project structure进去看,发现并没有spring-core的依赖 于是在相应的module下手动引入依赖后,项目运行正常... ...
Code:github.com/openai/lm-hu 目标是结合预训练模型与人类偏好学习。使用强化学习而不是监督学习来微调预训练的语言模型,使用根据人类对文本延续的偏好,训练奖励模型。 理论 对于LLM来说,预测当前第k个token的概率为前k-1个token概率的乘积 那么对于输入一个1000字的文本x,能够输出100字的总结y的概率可以表示为 ...
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I did change source code and this is what it looks like after change. You are right my action mask had error. Now its seems to be learning but rewards is increasing slowly. And i still have problem that mean length of episode is increasing even though I know can win fast in some case...
Decentralized Distributed Proximal Policy Optimization (DD-PPO) is a method for distributed reinforcement learning in resource-intensive simulated environments. DD-PPO is distributed (uses multiple machines), decentralized (lacks a centralized server), a