DeepMind Explores Deep RL for Brain and Behaviour Research

As a basis for modelling brain function, deep learning has in recent years been used to model systems in vision, audition, motor control, navigation, and cognitive control. In a new paper, DeepMind researchers call attention to another “fundamentally novel” development in AI research — deep reinforcement learning (deep RL) — which they believe also has vital implications for neuroscience and deserves more attention from neuroscientists.

The first neuroscience applications of supervised deep learning can be traced back to the 1980s. The increasing availability of more powerful computers over the past decade has renewed research efforts in applying AI approaches — especially supervised deep learning — to neuroscience.

Deep RL unites deep learning and reinforcement learning, a computational framework that has already had a substantial impact on neuroscience research. The DeepMind team proposes deep RL as a comprehensive framework for studying the interplay between learning, representation, and decision-making that can bring new set of research tools and a wide range of novel hypotheses to the brain sciences.

Although deep neural networks have proven an impressive model for neural representation, the team notes that related research has mostly utilized supervised training and has therefore provided little direct leverage on the big-picture problem of understanding motivated, goal-directed behaviour. At the same time, although RL has provided a powerful theory of the neural mechanisms of learning and decision making, it has until recently offered neuroscience little guidance in thinking through the problem of representation.

The researchers say deep RL offers neuroscience something new by demonstrating how RL and deep learning can fit together. While deep learning focuses on how representations are learned and RL on how rewards guide learning, “when deep learning and RL are integrated, each triggers new patterns of behaviour in the other, leading to computational phenomena unseen in either deep learning or RL on their own.”

The team highlights six areas where they believe deep RL may provide leverage for neuroscientific research: representation learning, model-based RL, memory, exploration, social cognition, and cognitive control and action hierarchies.

Explorations of deep RL in neuroscience have only just begun, and the researchers envision its increasing engagement in the future. They also note that since deep RL is a work in progress, the opportunity also exists for neuroscience research to influence deep RL, continuing the “virtuous circle” that has connected neuroscience and AI for decades.

The paper Deep Reinforcement Learning and its Neuroscientific Implications is on arXiv.

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Journalist: Yuan Yuan | Editor: Michael Sarazen

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