In this tutorial we'll study and build reinforcement learning models inspired by the brain. By the end you'll understand, and be able to construct, a series of simple but surprisingly powerful models of how agents learn to navigate spatial environments and find rewards.
Note: the colab renders better in Safari and Firefox than Chrome.
Figure 1: An agent has learn to navigate around a wall towards a hidden reward using place cell state features and a simple Q-value learning algorithm.
- Rescorla-Wagner Model (~60 mins)
- Temporal Difference Learning (~60 mins)
- Q-Values and Policy Improvement (~60 mins)
- State features and function approximation (~60 mins)
Solutions to the maths exercises can be found in a seperate solutions.ipynb notebook which may or may not be provided to you by the TAs.