Posts Tagged ‘Reinforcement Learning’

On March 4, I was contacted by the Xinhua News Agency to comment on the upcoming Go match between Google DeepMind’s AlphaGo algorithm and the top-Go player Lee Sedol. I am posting the questions and answers below:



This is a brief summary of the first part of the Deep RL workshop at NIPS 2015. I couldn’t get a seat for the second half…


Rich Sutton gave a tutorial on function approximation in RL. Rich being one of the pioneers of RL, I was looking forward to his insights. He started off with some inherent properties of reinforcement learning, which include:

  • Evaluative Feedback
  • Delayed consequences
  • Trial and error learning
  • Non-stationarity
  • Sequential problems
  • No human instruction


Samuel J. Gershman, Eric J. Horvitz, Joshua B. Tenenbaum:
Computational Rationality: A Converging Paradigm for Intelligence in Brains, Minds, and Machines
Science 349(6245): 273-278, 2015

The article provides a shared computation-based view on concepts in AI, cognitive science and neuroscience. Advances that address challenges of perception and action under uncertainty are discussed.

Manuel Watter, Jost Tobias Springenberg, Joschka Boedecker, Martin Riedmiller:
Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images
arXiv, 2015

This paper deals with the problem of model-based reinforcement learning (RL) from images. The idea behind model-based RL is to learn a model of the transition dynamics of the system/robot and use this model as a surrogate simulator. This is helpful if we want to minimize experiments with a (physical/mechanical) system. The added difficulty addressed in this paper is that this predictive transition model should be learned from raw images where only pixel information is available.

Central idea (more…)