333 citations · 684 across the 37 of their papers we have counts for
4 papers · 1 filter
Reinforcement Learning by Guided Safe Exploration
Qisong Yang, Thiago D. Simão, Nils Jansen +2
Safety is critical to broadening the application of reinforcement learning (RL). Often, we train RL agents in a controlled environment, such as a laboratory, before deploying them…
Diverse Projection Ensembles for Distributional Reinforcement Learning
Moritz A. Zanger, Wendelin Böhmer, Matthijs T. J. Spaan
In contrast to classical reinforcement learning (RL), distributional RL algorithms aim to learn the distribution of returns rather than their expected value. Since the nature of th…
The Role of Diverse Replay for Generalisation in Reinforcement Learning
Max Weltevrede, Matthijs T. J. Spaan, Wendelin Böhmer
In reinforcement learning (RL), key components of many algorithms are the exploration strategy and replay buffer. These strategies regulate what environment data is collected and t…
Bad Habits: Policy Confounding and Out-of-Trajectory Generalization in RL
Miguel Suau, Matthijs T. J. Spaan, Frans A. Oliehoek
Reinforcement learning agents tend to develop habits that are effective only under specific policies. Following an initial exploration phase where agents try out different actions,…