2 citations · 2 across the 4 of their papers we have counts for
4 papers
Gradient Iterated Temporal-Difference Learning
Théo Vincent, Kevin Gerhardt, Yogesh Tripathi +5
Temporal-difference (TD) learning is highly effective at controlling and evaluating an agent's long-term outcomes. Most approaches in this paradigm implement a semi-gradient update…
Bridging the Performance Gap Between Target-Free and Target-Based Reinforcement Learning
Théo Vincent, Yogesh Tripathi, Tim Faust +5
The use of target networks in deep reinforcement learning is a widely popular solution to mitigate the brittleness of semi-gradient approaches and stabilize learning. However, targ…
Eau De -Network: Adaptive Distillation of Neural Networks in Deep Reinforcement Learning
Théo Vincent, Tim Faust, Yogesh Tripathi +2
Recent works have successfully demonstrated that sparse deep reinforcement learning agents can be competitive against their dense counterparts. This opens up opportunities for rein…
InSaAF: Incorporating Safety through Accuracy and Fairness | Are LLMs ready for the Indian Legal Domain?
Yogesh Tripathi, Raghav Donakanti, Sahil Girhepuje +7
Recent advancements in language technology and Artificial Intelligence have resulted in numerous Language Models being proposed to perform various tasks in the legal domain ranging…