OPEB: Open Physical Environment Benchmark for Artificial Intelligence
원문 초록
Artificial Intelligence methods to solve continuous- control tasks have made significant progress in recent years. However, these algorithms have important limitations and still need significant improvement to be used in industry and real- world applications. This means that this area is still in an active research phase. To involve a large number of research groups, standard benchmarks are needed to evaluate and compare proposed algorithms. In this paper, we propose a physical environment benchmark framework to facilitate collaborative research in this area by enabling different research groups to integrate their designed benchmarks in a unified cloud-based repository and also share their actual implemented benchmarks via the cloud. We demonstrate the proposed framework using an actual implementation of the classical mountain-car example and present the results obtained using a Reinforcement Learning algorithm.
함께 살펴볼 논문
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Sparse-Reg: Improving Sample Complexity in Offline Reinforcement Learning using Sparsity
DR-SAC: Distributionally Robust Soft Actor-Critic for Reinforcement Learning under Uncertainty
Supervised Fine Tuning on Curated Data is Reinforcement Learning (and can be improved)
Scaling Algorithm Distillation for Continuous Control with Mamba
Reinforcement Learning via Implicit Imitation Guidance
Safe Planning and Policy Optimization via World Model Learning
토론
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