23 citations · 97 across the 14 of their papers we have counts for
10 papers · 1 filter
Eigensubspace of Temporal-Difference Dynamics and How It Improves Value Approximation in Reinforcement Learning
Qiang He, Tianyi Zhou, Meng Fang +1
We propose a novel value approximation method, namely Eigensubspace Regularized Critic (ERC) for deep reinforcement learning (RL). ERC is motivated by an analysis of the dynamics o…
Interpretable Reward Redistribution in Reinforcement Learning: A Causal Approach
Yudi Zhang, Yali Du, Biwei Huang +4
A major challenge in reinforcement learning is to determine which state-action pairs are responsible for future rewards that are delayed. Reward redistribution serves as a solution…
Superposing Many Tickets into One: A Performance Booster for Sparse Neural Network Training
Lu Yin, Vlado Menkovski, Meng Fang +5
Recent works on sparse neural network training (sparse training) have shown that a compelling trade-off between performance and efficiency can be achieved by training intrinsically…
Rethinking Goal-conditioned Supervised Learning and Its Connection to Offline RL
Rui Yang, Yiming Lu, Wenzhe Li +6
Solving goal-conditioned tasks with sparse rewards using self-supervised learning is promising because of its simplicity and stability over current reinforcement learning (RL) algo…
Safe Exploration by Solving Early Terminated MDP
Hao Sun, Ziping Xu, Meng Fang +4
Safe exploration is crucial for the real-world application of reinforcement learning (RL). Previous works consider the safe exploration problem as Constrained Markov Decision Proce…
MHER: Model-based Hindsight Experience Replay
Rui Yang, Meng Fang, Lei Han +3
Solving multi-goal reinforcement learning (RL) problems with sparse rewards is generally challenging. Existing approaches have utilized goal relabeling on collected experiences to…
TLeague: A Framework for Competitive Self-Play based Distributed Multi-Agent Reinforcement Learning
Peng Sun, Jiechao Xiong, Lei Han +5
Competitive Self-Play (CSP) based Multi-Agent Reinforcement Learning (MARL) has shown phenomenal breakthroughs recently. Strong AIs are achieved for several benchmarks, including D…
Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Games
Yunqiu Xu, Meng Fang, Ling Chen +3
We study reinforcement learning (RL) for text-based games, which are interactive simulations in the context of natural language. While different methods have been developed to repr…
Zeroth-Order Supervised Policy Improvement
Hao Sun, Ziping Xu, Yuhang Song +4
Policy gradient (PG) algorithms have been widely used in reinforcement learning (RL). However, PG algorithms rely on exploiting the value function being learned with the first-orde…
Arena: a toolkit for Multi-Agent Reinforcement Learning
Qing Wang, Jiechao Xiong, Lei Han +5
We introduce Arena, a toolkit for multi-agent reinforcement learning (MARL) research. In MARL, it usually requires customizing observations, rewards and actions for each agent, cha…