4 accepted papers
We present AutoFocus-IL, a simple yet effective method to improve data efficiency and generalization in visual imitation learning by guiding policies to attend to task-relevant features rather than distractors and spurious correlations. Saliency regularization has emerged as a promising way to achie…
Imitation Learning (IL) is a widely adopted approach which enables agents to learn from human expert demonstrations by framing the task as a supervised learning problem. However, IL often suffers from causal confusion, where agents misinterpret spurious correlations as causal relationships, leading
Communication is an important factor that en-ables agents to work cooperatively in multi-agent reinforcement learning (MARL) contexts. Prior work used continuous message communication whose high representational capacity comes at the expense of interpretability. Allowing agents to learn their own di…
Deep reinforcement learning has generated superhuman AI in competitive games such as Go and StarCraft. Can similar learning techniques create a superior AI teammate for human-machine collaborative games? Will humans prefer AI teammates that improve objective team performance or those that improve su…