arXiv:2201.05433
공식 코드 · Papers with Code
Comparing Model-free and Model-based Algorithms for Offline Reinforcement Learning
원문 초록
Offline reinforcement learning (RL) Algorithms are often designed with environments such as MuJoCo in mind, in which the planning horizon is extremely long and no noise exists. We compare model-free, model-based, as well as hybrid offline RL approaches on various industrial benchmark (IB) datasets to test the algorithms in settings closer to real world problems, including complex noise and partially observable states. We find that on the IB, hybrid approaches face severe difficulties and that simpler algorithms, such as rollout based algorithms or model-free algorithms with simpler regularizers perform best on the datasets.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Deep Reinforcement Learning with Gradient Eligibility Traces
Sparse-Reg: Improving Sample Complexity in Offline Reinforcement Learning using Sparsity
DR-SAC: Distributionally Robust Soft Actor-Critic for Reinforcement Learning under Uncertainty
From Novelty to Imitation: Self-Distilled Rewards for Offline Reinforcement Learning
Aligning Humans and Robots via Reinforcement Learning from Implicit Human Feedback
Policy-Based Trajectory Clustering in Offline Reinforcement Learning
토론
이 논문과 연결된 포럼 글이 아직 없습니다.