3 citations · 5 across the 3 of their papers we have counts for
Showing cs.LGShow all
3 papers · 1 filter
cs.LG2023
Prediction and Control in Continual Reinforcement Learning
Nishanth Anand, Doina Precup
Temporal difference (TD) learning is often used to update the estimate of the value function which is used by RL agents to extract useful policies. In this paper, we focus on value…
cs.LG2021★ 3 cited
Preferential Temporal Difference Learning
Nishanth Anand, Doina Precup
Temporal-Difference (TD) learning is a general and very useful tool for estimating the value function of a given policy, which in turn is required to find good policies. Generally…
cs.LG2019★ 2 cited
Recurrent Value Functions
Pierre Thodoroff, Nishanth Anand, Lucas Caccia +2
Despite recent successes in Reinforcement Learning, value-based methods often suffer from high variance hindering performance. In this paper, we illustrate this in a continuous con…