3.1.1 Vectorized architecture 【Code-level Optimizations】 3.1.2 Orthogonal Initialization of Weights and Constant Initialization of biases 【Code-level Optimizations】 3.1.3 The Adam Optimizer’s Epsilon Param
针对PPO的一些Code-level性能优化技巧 Intro 这篇blog是我在看过Logan等人的“implementation matters in deep policy gradients: a case study on ppo and trpo“之后的总结。 reward clipping clip the rewards within a preset range( usually [-5,5] or [-10,10]) observation clipping The state are first...
现在到处用的都是Deep RL,如果不仔细理解每一个技巧能够带来的性能影响,而是code tricks以一把梭的形式全部扔到炉子里去炼,则得出的丹药所具备的功能都不知道来源于哪一味原材料,这是非常不严谨的。 参考文献: 【1】Implementation Matters in Deep RL: A Case Study on PPO and TRPO, openreview.net/forum?
(2)reward scaling:在《PPO-Implementation matters in deep policy gradients A case study on PPO and TRPO》[3]这篇论文中,作者中提出了一种名叫reward scaling的方法,如图5所示。reward scaling与reward normalization的区别在于,reward scaling是动态计算一个standard deviation of a rolling discounted sum of th...
Code Issues Pull requests Clean, Robust, and Unified PyTorch implementation of popular Deep Reinforcement Learning (DRL) algorithms (Q-learning, Duel DDQN, PER, C51, Noisy DQN, PPO, DDPG, TD3, SAC, ASL) machine-learning reinforcement-learning asl deep-reinforcement-learning q-learning pytorch ...
In code implementation, the entropy bonus is equivalent to a negative term on the loss function, so the model tends to optimize it to as large a value as possible. Delta is a hyperparameter that must be carefully tuned to prevent training collapse (our experiments fail with only a 10% ...
下面给出了每个算法的最终参数,我们的代码发布中提供了更详细的说明:https://github.com/MadryLab/implementation-matters。 我们绘制的所有误差条都是通过自举采样获得的95%置信区间。 7https://github.com/openai/baselines A.2 PPO CODE-LEVEL OPTIMIZATIONS A.3 TRUST REGION OPTIMIZATION...
The source code for the blog post The 37 Implementation Details of Proximal Policy Optimization - vwxyzjn/ppo-implementation-details
eval_dataset=prepare_dataset(eval_dataset, tokenizer), ) trainer.train() 最后对显存做了一下PPO的实验,使用deepspeed的zero3,可以降低很多: Reference 1、https://huggingface.co/blog/the_n_implementation_details_of_rlhf_with_ppo 作者:淡水,
To examine the performance of the proposed approach, we implement the approach based on the OpenAI stable baselines [10] with all necessary code modifications. Our codes can be found in [11]. We conduct two experiments to evaluate the performance of removing invalid actions [12]. The first ex...