Reinforcement Learning
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.
Policy Gradient for Rectangular Robust Markov Decision Processes
However, they do not account for transition uncertainty, whereas learning robust policies can be computationally expensive.
