600 citations · 712 across the 15 of their papers we have counts for
9 papers · 1 filter
Pre-Trained Image Encoder for Generalizable Visual Reinforcement Learning
Zhecheng Yuan, Zhengrong Xue, Bo Yuan +4
Learning generalizable policies that can adapt to unseen environments remains challenging in visual Reinforcement Learning (RL). Existing approaches try to acquire a robust represe…
Phasic Self-Imitative Reduction for Sparse-Reward Goal-Conditioned Reinforcement Learning
Yunfei Li, Tian Gao, Jiaqi Yang +2
It has been a recent trend to leverage the power of supervised learning (SL) towards more effective reinforcement learning (RL) methods. We propose a novel phasic approach by alter…
Continuously Discovering Novel Strategies via Reward-Switching Policy Optimization
Zihan Zhou, Wei Fu, Bingliang Zhang +1
We present Reward-Switching Policy Optimization (RSPO), a paradigm to discover diverse strategies in complex RL environments by iteratively finding novel policies that are both loc…
Variational Automatic Curriculum Learning for Sparse-Reward Cooperative Multi-Agent Problems
Jiayu Chen, Yuanxin Zhang, Yuanfan Xu +5
We introduce a curriculum learning algorithm, Variational Automatic Curriculum Learning (VACL), for solving challenging goal-conditioned cooperative multi-agent reinforcement learn…
DAIR: Disentangled Attention Intrinsic Regularization for Safe and Efficient Bimanual Manipulation
Minghao Zhang, Pingcheng Jian, Yi Wu +2
We address the problem of safely solving complex bimanual robot manipulation tasks with sparse rewards. Such challenging tasks can be decomposed into sub-tasks that are accomplisha…
Solving Compositional Reinforcement Learning Problems via Task Reduction
Yunfei Li, Yilin Wu, Huazhe Xu +2
We propose a novel learning paradigm, Self-Imitation via Reduction (SIR), for solving compositional reinforcement learning problems. SIR is based on two core ideas: task reduction…
The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games
Chao Yu, Akash Velu, Eugene Vinitsky +4
Proximal Policy Optimization (PPO) is a ubiquitous on-policy reinforcement learning algorithm but is significantly less utilized than off-policy learning algorithms in multi-agent…
BeBold: Exploration Beyond the Boundary of Explored Regions
Tianjun Zhang, Huazhe Xu, Xiaolong Wang +4
Efficient exploration under sparse rewards remains a key challenge in deep reinforcement learning. To guide exploration, previous work makes extensive use of intrinsic reward (IR).…
Multi-Agent Collaboration via Reward Attribution Decomposition
Tianjun Zhang, Huazhe Xu, Xiaolong Wang +4
Recent advances in multi-agent reinforcement learning (MARL) have achieved super-human performance in games like Quake 3 and Dota 2. Unfortunately, these techniques require orders-…