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:

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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…

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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

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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.
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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.

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