5 citations · 9 across the 3 of their papers we have counts for
3 papers
cs.LG2023★ 2 cited
Reasoning with Latent Diffusion in Offline Reinforcement Learning
Siddarth Venkatraman, Shivesh Khaitan, Ravi Tej Akella +3
Offline reinforcement learning (RL) holds promise as a means to learn high-reward policies from a static dataset, without the need for further environment interactions. However, a…
cs.LG2023★ 2 cited
Model-based Dynamic Shielding for Safe and Efficient Multi-Agent Reinforcement Learning
Wenli Xiao, Yiwei Lyu, John Dolan
Multi-Agent Reinforcement Learning (MARL) discovers policies that maximize reward but do not have safety guarantees during the learning and deployment phases. Although shielding wi…
cs.LG2022★ 5 cited
Addressing Optimism Bias in Sequence Modeling for Reinforcement Learning
Adam Villaflor, Zhe Huang, Swapnil Pande +2
Impressive results in natural language processing (NLP) based on the Transformer neural network architecture have inspired researchers to explore viewing offline reinforcement lear…