The study of what actions an agent should take in a stochastic environment in order to maximize a cumulative reward.

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'Model-free' learning in humans

In reinforcement learning, there is a stark distinction between model-based and model-free learning algorithms, where model-free methods don't make use any explicit information about the dynamics of ...
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269 views

Refinements of Rescorla-Wagner model of classical conditioning

The Rescorla-Wagner model is one of the most commonly discussed mathematical models of classical conditioning. It was wildly popular when it came out in 1972, and very successful. The same math, is ...
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596 views

How does goal-tracking and sign-tracking behaviour vary across species?

In Pavlonian (Classical) Conditioning, conditioned responses of an animal may vary. Some animals focus on the unconditioned stimulus (ie. food / location of food) while others may focus on the ...
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Why has behaviourism fallen out of favour?

Despite the catchy title, I'm not interested in personal opinions here. I am however, interested in feedback on how to better phrase the question so as to avoid personal opinions. After some ...
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How do mammals explore state spaces in reinforcement learning tasks?

Reinforcement learning is the act of learning how to preform a task given punishment and reward. A "state-space" is the space of choices in a context. When performing a reinforcement learning task, is ...
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32 views

Frequency estimation in binary prediction task

In a binary prediction task, people often match choice probabilities to outcome probabilities (a phenomenon known as probability matching). However, under certain circumstances (eg, existence of a ...
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Brain areas active while learning hierarchical structure of a problem

There are multiple examples in the machine learning literature of trying to learn the hierarchical structure of a reinforcement learning problem, however have there been any papers tying this learning ...
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Structure for General Intelligence

I've just read Dan Rasmussen's paper on general intelligence and I was wondering what other approaches for complex, scalable and adaptable learning have been tried in the past? This question's scope ...