Classic and Modern Reinforcement Learning

Sun, 10 Dec 2017 00:00:00 +0000

At the Deep Reinforcement Learning Symposium at NIPS this year I had the pleasure of shaking hands with Dimitri Bertsekas, whose work has been foundational to the mathematical theory of reinforcement learning. I still turn to Neuro-Dynamic Programming (Bertsekas and Tsitsiklis, 1996) when searching for tools to explain sample-based algorithms such as TD. Earlier this summer I read parts of Dynamic Programming and Optimal Control: Volume 2, which is full of gems that could grow into a full-blown NIPS or ICML paper (try it yourself). You can imagine my excitement at finally meeting the legend. So when he candidly asked me, “What’s deep reinforcement learning, anyway?” it was clear that “Q-learning with neural networks” wasn’t going to cut it.

Deep-Rl on Marc G. Bellemare

Classic and Modern Reinforcement Learning