177 citations · 263 across the 17 of their papers we have counts for
4 papers · 1 filter
T3S: Improving Multi-Task Reinforcement Learning with Task-Specific Feature Selector and Scheduler
Yuanqiang Yu, Tianpei Yang, Yongliang Lv +2
Multi-task reinforcement learning (MTRL) is a technique to train multiple tasks simultaneously, where previous works usually train a single model to solve different tasks by sharin…
Learning to Shape Rewards using a Game of Two Partners
David Mguni, Taher Jafferjee, Jianhong Wang +9
Reward shaping (RS) is a powerful method in reinforcement learning (RL) for overcoming the problem of sparse or uninformative rewards. However, RS typically relies on manually engi…
Efficient Deep Reinforcement Learning via Adaptive Policy Transfer
Tianpei Yang, Jianye Hao, Zhaopeng Meng +8
Transfer Learning (TL) has shown great potential to accelerate Reinforcement Learning (RL) by leveraging prior knowledge from past learned policies of relevant tasks. Existing tran…
Learning Action-Transferable Policy with Action Embedding
Yu Chen, Yingfeng Chen, Zhipeng Hu +4
Transfer learning (TL) is a promising way to improve the sample efficiency of reinforcement learning. However, how to efficiently transfer knowledge across tasks with different sta…