Reinforcement Learning
Zero-sum Polymatrix Markov Games: Equilibrium Collapse and Efficient Computation of Nash Equilibria
One such example is two-player zero-sum Markov games, in which efficient ways to compute a Nash equilibrium are known. Inspired by zero-sum polymatrix normal-form games (Cai et al., 2016), we define a class of zero-sum
Learning Better with Less: Effective Augmentation for Sample-Efficient Visual Reinforcement Learning
DA operations, we reveal that both ample spatial diversity and slight hardness are indispensable.
Learning from Visual Observation via Offline Pretrained State-to-Go Transformer Bohan Zhou 1 Ke Li
Learning from visual observation (LfVO), aiming at recovering policies from only visual observation data, is promising yet a challenging problem.
CE-NAS: An End-to-End Carbon-Efficient Neural Architecture Search Framework
This work presents a novel approach to neural architecture search (NAS) that aims to increase carbon efficiency for the model design process.
Selectively Sharing Experiences Improves Multi-Agent Reinforcement Learning
We present a novel multi-agent RL approach, Selective Multi-Agent Prioritized Experience Relay, in which agents share with other agents a limited number of transitions they observe during training.