1 citations · 1 across the 9 of their papers we have counts for
5 papers · 1 filter
ACSAC: Adaptive Chunk Size Actor-Critic with Causal Transformer Q-Network
Qian Chen, Junqiao Zhao, Hongtu Zhou +4
Long-horizon, sparse-reward tasks pose a fundamental challenge for reinforcement learning, since single-step TD learning suffers from bootstrapping error accumulation across succes…
ASTRO: Adaptive Stitching via Dynamics-Guided Trajectory Rollouts
Hang Yu, Di Zhang, Qiwei Du +5
Offline reinforcement learning (RL) enables agents to learn optimal policies from pre-collected datasets. However, datasets containing suboptimal and fragmented trajectories presen…
Scrutinize What We Ignore: Reining In Task Representation Shift Of Context-Based Offline Meta Reinforcement Learning
Hai Zhang, Boyuan Zheng, Tianying Ji +4
Offline meta reinforcement learning (OMRL) has emerged as a promising approach for interaction avoidance and strong generalization performance by leveraging pre-collected data and…
Potentially Optimal Joint Actions Recognition for Cooperative Multi-Agent Reinforcement Learning
Chang Huang, Shatong Zhu, Junqiao Zhao +6
Value function factorization is widely used in cooperative multi-agent reinforcement learning (MARL). Existing approaches often impose monotonicity constraints between the joint ac…
Towards an Information Theoretic Framework of Context-Based Offline Meta-Reinforcement Learning
Lanqing Li, Hai Zhang, Xinyu Zhang +4
As a marriage between offline RL and meta-RL, the advent of offline meta-reinforcement learning (OMRL) has shown great promise in enabling RL agents to multi-task and quickly adapt…