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cs.LG2019
Sample-Efficient Model-Free Reinforcement Learning with Off-Policy Critics
Denis Steckelmacher, Hélène Plisnier, Diederik M. Roijers +1
Value-based reinforcement-learning algorithms provide state-of-the-art results in model-free discrete-action settings, and tend to outperform actor-critic algorithms. We argue that…
cs.LG2018★ 1 cited
Directed Policy Gradient for Safe Reinforcement Learning with Human Advice
Hélène Plisnier, Denis Steckelmacher, Tim Brys +2
Many currently deployed Reinforcement Learning agents work in an environment shared with humans, be them co-workers, users or clients. It is desirable that these agents adjust to p…