3 citations · 3 across the 2 of their papers we have counts for
2 papers
cs.AI2023
Bandit-Based Policy Invariant Explicit Shaping for Incorporating External Advice in Reinforcement Learning
Yash Satsangi, Paniz Behboudian
A key challenge for a reinforcement learning (RL) agent is to incorporate external/expert1 advice in its learning. The desired goals of an algorithm that can shape the learning of…
cs.LG2020★ 3 cited
Useful Policy Invariant Shaping from Arbitrary Advice
Paniz Behboudian, Yash Satsangi, Matthew E. Taylor +2
Reinforcement learning is a powerful learning paradigm in which agents can learn to maximize sparse and delayed reward signals. Although RL has had many impressive successes in com…