具体实现: 首先针对model-based 以及 model-free方法进行初步验证,作者使用MBPO方法以及SAC方法进行对比实验,实验的结果表明,model-based方法MBPO在offline RL任务上有较高的性能,因此可以基于该方法进行改进,实现offlineRL 2. 随后,针对MBPO进行具体分析,model-based方法在执行offlineRL时存在缺陷,无法有效地进行batch ...
以前的一些offline方法(model-free)衡量误差会用到out-of-distribution的动作,但是考虑状态却只考虑offline dataset中存在的,而不考虑out-of-distribution的情况。本文指出offline RL算法应该具有脱离数据支持去学习更好策略的能力,因为 ① 提供的数据集其覆盖的状态和动作通常都是次优的 ② 目标任务与数据中所执行的任...
作者提出Model-based Offline Policy Optimization (MOPO)算法,用model based的方法来做offline RL,同时通过给reward添加惩罚项(soft reward penalty)来描述环境转移的不确定性(applying them with rewards artificially penalized by the uncertainty of the dynamics.)这种方式相当于在泛化性和风险之间做tradeoff。作者的意...
Hence, a model-based offline adaptive policy optimization with episodic memory is proposed in this work to improve generalization of the policy. Inspired by active learning, constraint strength is proposed to trade off the return and risk adaptively to balance the robustness and generalization ability...
Code to reproduce the experiments inMOPO: Model-based Offline Policy Optimization. Installation InstallMuJoCo 2.0at~/.mujoco/mujoco200and copy your license key to~/.mujoco/mjkey.txt Create a conda environment and install mopo cd mopo conda env create -f environment/gpu-env.yml conda activate mop...
This is a re-implementation of the offline model-based RL algorithm MOPO all by pytorch(including dynamics and mopo algo)as described in the following paper:MOPO: Model-based Offline Policy Optimization The performance of model-based RL algorithm greatly depends on the implementation of the ensemble...
文章要点:这篇文章用model based方法去做offline RL。主要分为两步,第一步是用offline data学一个pessimistic MDP (P-MDP),第二步就是用这个P-MDP去学一个near-optimal policy。P-MDP的性质保证了这个near-optimal policy是真实环境里的performance的lower bound。具体来说,因为dataset不可能覆盖整个状态动作空间,...
Moor: Model-based offline policy optimization with a risk dynamics model Offline reinforcement learning (RL) has been widely used in safety-critical domains by avoiding dangerous and costly online interaction. A significant chal... X Su,P Li,S Chen - 《Complex & Intelligent Systems》 被引量: ...
Moor: Model-based offline policy optimization with a risk dynamics model Offline reinforcement learning (RL) has been widely used in safety-critical domains by avoiding dangerous and costly online interaction. A significant chal... X Su,P Li,S Chen - 《Complex & Intelligent Systems》 被引量: ...
如下图所示,offline RL只需要直接部署在现实环境中,故deployment efficiency非常高,而on-policy算法策略一般采集整条轨迹的数据才会更新,故deployment efficiency次之,而off-policy算法一般采集一条数据就会发生更新,故deployment efficiency非常低(有点和采样效率的概念相对)...