David Yu-tung Hui

Double Gumbel Q-Learning

2023-12-11

Neural Information Processing Systems (Accept (spotlight))

doi.org

openreview.net

Combating False Negatives in Adversarial Imitation Learning

Konrad Żołna

Chitwan Saharia

Léonard Boussioux

David Yu-tung Hui

Maxime Chevalier-Boisvert

Dzmitry Bahdanau

Yoshua Bengio

In adversarial imitation learning, a discriminator is trained to differentiate agent episodes from expert demonstrations representing the de… (voir plus)sired behavior. However, as the trained policy learns to be more successful, the negative examples (the ones produced by the agent) become increasingly similar to expert ones. Despite the fact that the task is successfully accomplished in some of the agent's trajectories, the discriminator is trained to output low values for them. We hypothesize that this inconsistent training signal for the discriminator can impede its learning, and consequently leads to worse overall performance of the agent. We show experimental evidence for this hypothesis and that the ‘False Negatives’ (i.e. successful agent episodes) significantly hinder adversarial imitation learning, which is the first contribution of this paper. Then, we propose a method to alleviate the impact of false negatives and test it on the BabyAI environment. This method consistently improves sample efficiency over the baselines by at least an order of magnitude.

2021-07-17

International Joint Conference on Neural Networks (publié)

doi.org

arxiv.org

Combating False Negatives in Adversarial Imitation Learning (Student Abstract)

Konrad Żołna

Chitwan Saharia

Léonard Boussioux

David Yu-tung Hui

Maxime Chevalier-Boisvert

Dzmitry Bahdanau

Yoshua Bengio