Topic: Model-based reinforcement learning

Voluntary actions are actions that agents choose to make. Volition is the set of cognitive processes that implement such choice and initiation. These processes are often held essential to modern societies, because they form the cognitive underpinning for concepts of individual autonomy and individual responsibility. Nevertheless, psychology and neuroscience have struggled to define volition, and have also struggled to study it scientifically. Laboratory experiments on volition, such as those of Libet, have been criticised, often rather naively, as focussing exclusively on meaningless actions,

In his bestseller 'Thinking, Fast and Slow', Daniel Kahneman popularized the idea that there are two fundamentally different process of thought: a 'System 1' process that is unconscious and instinctive, and a 'System 2' process that is deliberative and requires conscious attention. There is a growing recognition that machine learning is mostly stuck at the 'System 1' level of cognition, and that moving to 'System 2' methods are key to solving long-standing challenges such as out-of-distribution generalization. In this talk, AlphaZero will be used as a case-study of the power of combining 'Syst

Model-based approaches to control and decision making have long held the promise of being more powerful and data efficient than model-free counterparts. However, success with model-based methods has been limited to those cases where a perfect model can be queried. The game of Go was mastered by AlphaGo using a combination of neural networks and the MCTS planning algorithm. But planning required a perfect representation of the game rules. I will describe new algorithms that instead leverage deep neural networks to learn models of the environment which are then used to plan, and update policy an