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1
Intro
2
Pavlovian conditioning
3
Associative learning theory
4
Examples
5
Probabilistic interpretation
6
What's missing?
7
The Kalman filter
8
Some intuitions
9
Backward blocking
10
Modeling recovery phenomena
11
Sequential decision problems
12
Long-term reward prediction
13
Prediction errors
14
Temporal difference learning
15
Stimulus representation
16
Behavioral implications of TD
17
Dopamine
Description:
Explore the fundamentals of reinforcement learning and its applications in psychology and neuroscience through this comprehensive tutorial led by Prof. Sam Gershman from Harvard University. Delve into key concepts such as Pavlovian conditioning, associative learning theory, and temporal difference learning. Engage in hands-on exercises to understand how simple algorithms explain animal learning and dopamine neuron firing. Access supplementary materials, including slides, references, and code, to enhance your learning experience. Gain insights into probabilistic interpretations, the Kalman filter, sequential decision problems, and the behavioral implications of temporal difference learning.

Reinforcement Learning

MITCBMM
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