9 citations · 16 across the 4 of their papers we have counts for
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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
Intrinsic Exploration as Multi-Objective RL
Philippe Morere, Fabio Ramos
Intrinsic motivation enables reinforcement learning (RL) agents to explore when rewards are very sparse, where traditional exploration heuristics such as Boltzmann or e-greedy woul…
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…