94 citations · 158 across the 7 of their papers we have counts for
5 papers · 1 filter
Neural-to-Tree Policy Distillation with Policy Improvement Criterion
Zhao-Hua Li, Yang Yu, Yingfeng Chen +3
While deep reinforcement learning has achieved promising results in challenging decision-making tasks, the main bones of its success --- deep neural networks are mostly black-boxes…
Learning to Utilize Shaping Rewards: A New Approach of Reward Shaping
Yujing Hu, Weixun Wang, Hangtian Jia +5
Reward shaping is an effective technique for incorporating domain knowledge into reinforcement learning (RL). Existing approaches such as potential-based reward shaping normally ma…
Reinforcement Learning Experience Reuse with Policy Residual Representation
Wen-Ji Zhou, Yang Yu, Yingfeng Chen +4
Experience reuse is key to sample-efficient reinforcement learning. One of the critical issues is how the experience is represented and stored. Previously, the experience can be st…
Deep Multi-Agent Reinforcement Learning with Discrete-Continuous Hybrid Action Spaces
Haotian Fu, Hongyao Tang, Jianye Hao +3
Deep Reinforcement Learning (DRL) has been applied to address a variety of cooperative multi-agent problems with either discrete action spaces or continuous action spaces. However,…
Hierarchical Deep Multiagent Reinforcement Learning with Temporal Abstraction
Hongyao Tang, Jianye Hao, Tangjie Lv +8
Multiagent reinforcement learning (MARL) is commonly considered to suffer from non-stationary environments and exponentially increasing policy space. It would be even more challeng…