26 citations · 123 across the 17 of their papers we have counts for
11 papers · 1 filter
GEAR: A GPU-Centric Experience Replay System for Large Reinforcement Learning Models
Hanjing Wang, Man-Kit Sit, Congjie He +5
This paper introduces a distributed, GPU-centric experience replay system, GEAR, designed to perform scalable reinforcement learning (RL) with large sequence models (such as transf…
Is Risk-Sensitive Reinforcement Learning Properly Resolved?
Ruiwen Zhou, Minghuan Liu, Kan Ren +3
Due to the nature of risk management in learning applicable policies, risk-sensitive reinforcement learning (RSRL) has been realized as an important direction. RSRL is usually achi…
An Empirical Study on Google Research Football Multi-agent Scenarios
Yan Song, He Jiang, Zheng Tian +6
Few multi-agent reinforcement learning (MARL) research on Google Research Football (GRF) focus on the 11v11 multi-agent full-game scenario and to the best of our knowledge, no open…
Bridging the Sim-to-Real Gap from the Information Bottleneck Perspective
Haoran He, Peilin Wu, Chenjia Bai +5
Reinforcement Learning (RL) has recently achieved remarkable success in robotic control. However, most works in RL operate in simulated environments where privileged knowledge (e.g…
Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning
Haoran He, Chenjia Bai, Kang Xu +5
Diffusion models have demonstrated highly-expressive generative capabilities in vision and NLP. Recent studies in reinforcement learning (RL) have shown that diffusion models are a…
Honor of Kings Arena: an Environment for Generalization in Competitive Reinforcement Learning
Hua Wei, Jingxiao Chen, Xiyang Ji +11
This paper introduces Honor of Kings Arena, a reinforcement learning (RL) environment based on Honor of Kings, one of the world's most popular games at present. Compared to other e…
Reinforcement Learning with Automated Auxiliary Loss Search
Tairan He, Yuge Zhang, Kan Ren +5
A good state representation is crucial to solving complicated reinforcement learning (RL) challenges. Many recent works focus on designing auxiliary losses for learning informative…
A Survey on Model-based Reinforcement Learning
Fan-Ming Luo, Tian Xu, Hang Lai +3
Reinforcement learning (RL) solves sequential decision-making problems via a trial-and-error process interacting with the environment. While RL achieves outstanding success in play…
Bootstrapped Transformer for Offline Reinforcement Learning
Kerong Wang, Hanye Zhao, Xufang Luo +3
Offline reinforcement learning (RL) aims at learning policies from previously collected static trajectory data without interacting with the real environment. Recent works provide a…
Towards Applicable Reinforcement Learning: Improving the Generalization and Sample Efficiency with Policy Ensemble
Zhengyu Yang, Kan Ren, Xufang Luo +5
It is challenging for reinforcement learning (RL) algorithms to succeed in real-world applications like financial trading and logistic system due to the noisy observation and envir…
NeoRL: A Near Real-World Benchmark for Offline Reinforcement Learning
Rongjun Qin, Songyi Gao, Xingyuan Zhang +5
Offline reinforcement learning (RL) aims at learning a good policy from a batch of collected data, without extra interactions with the environment during training. However, current…