2. TD3+BC原理 2.1 TD3+BC相比于其他的优势 下图是TD3+BC算法相对于CQL、Fish-BRC算法的复杂性对比,从表中我们可以看到CQL和Fish-BRC在算法(algorithmic)上有了很多的变种,使用生成网络,近似logsumexp等,而TD3+BC仅仅添加了一个BC term和Normalized state,足够的简单。 2.2 理论部分 对于经典的DDPG、TD3等...
使用target Network和target policy smoothing regularization 希望学到的价值函数对action更平滑,每次更新Expect return需要对policy输出的动作增加noise y=r+γQϕ(s′,πψ(s′)+ϵ)ϵ|N(0,σ) TD3-BC 更新策略参数增加一项BC-loss 从数据集D中采样(s,a),希望策略π和数据集近似 提升Q值估计 maxQ(s,...
reinforcement-learning flax cql single-file jax awac iql offline-rl offline-reinforcement-learning d4rl decision-transformer td3bc Updated Aug 15, 2024 Python Improve this page Add a description, image, and links to the td3bc topic page so that developers can more easily learn about it....
比较了算法们的训练时间,TD3 + BC 训练时间最短。 图2 3 显示的稳定性问题。(见上面 section 4 的笔记) ablation: 师兄提到的一点:可以预见的是,删除 state normalization 的影响是最小的,但它仍然有好处,并且只是一个微小的调整。对应 Figure 5 最后一张图。
High-quality single-file implementations of SOTA Offline and Offline-to-Online RL algorithms: AWAC, BC, CQL, DT, EDAC, IQL, SAC-N, TD3+BC, LB-SAC, SPOT, Cal-QL, ReBRAC arxiv.org/abs/2210.07105 Topics reinforcement-learning gym offline-reinforcement-learning d4rl Resources Readme ...
1 个内容 离线强化学习(Offline RL)系列3: (算法篇) TD3+BC 算法详解与实现(经验篇) Jensen Wang 在读Ph.D,研究深度强化学习 Jensen Wang: 【更新日志】论文信息:Scott Fujimoto, Shixiang Shane Gu: “A Minimalist Approach to Offline Reinforc… ...
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“When we started just running the ball like that, we weren’t going to come off of that,” BC coach Steve Addazio said. “I’ve been around a fair amount of time and that was an unbelievable performance by those guys and we just had to keep feeding them the rock.” ...