7 citations · 12 across the 2 of their papers we have counts for
3 papers
cs.LG2021
Learning from Demonstration without Demonstrations
Tom Blau, Gilad Francis, Philippe Morere
State-of-the-art reinforcement learning (RL) algorithms suffer from high sample complexity, particularly in the sparse reward case. A popular strategy for mitigating this problem i…
cs.LG2020★ 5 cited
Reinforcement Learning with Probabilistically Complete Exploration
Philippe Morere, Gilad Francis, Tom Blau +1
Balancing exploration and exploitation remains a key challenge in reinforcement learning (RL). State-of-the-art RL algorithms suffer from high sample complexity, particularly in th…
cs.LG2019★ 7 cited
Bayesian Curiosity for Efficient Exploration in Reinforcement Learning
Tom Blau, Lionel Ott, Fabio Ramos
Balancing exploration and exploitation is a fundamental part of reinforcement learning, yet most state-of-the-art algorithms use a naive exploration protocol like -greedy. This…