Reinforcement Learning: SARSA Algorithm

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Policy Gradient Methods: Single Neuron Example

Covers policy gradient methods using a single neuron with binary output.

Advanced Machine Learning: Discrete Reinforcement Learning

Introduces the basics of Reinforcement Learning, covering discrete states, actions, policies, value functions, MDPs, and optimal policies.

Learning to Find a Goal

Delves into a biologically inspired version of Reinforcement Learning, focusing on maze navigation and the implementation of spiking neurons.

Model-Based Deep Reinforcement Learning: Monte Carlo Tree Search

Explores model-based deep reinforcement learning, focusing on Monte Carlo Tree Search and its applications in game strategies and decision-making processes.

Data-driven Reinforcement Learning

Discusses challenges in AI systems, supervised learning limitations, and the necessity of data-driven methods in reinforcement learning.

Reinforcement Learning Fundamentals

Delves into the fundamentals of reinforcement learning, discussing states, actions, rewards, policies, and neural network applications.

Introduction to Reinforcement Learning: Key Concepts and Applications

Introduces reinforcement learning, covering its definitions, applications, and theoretical foundations, while outlining the course structure and objectives.

Principled Reinforcement Learning with Human Feedback

Delves into Reinforcement Learning with Human Feedback, discussing convergence of estimators and introducing a pessimistic approach for improved performance.

Relation of SARSA and Bellman equation

Explores the relation between fluctuating Q-values in SARSA and the Bellman equation through expectations and policy constancy.

Actor-Critic Architecture and Advantage-Actor-Critic

Explores actor-critic networks and the advantage actor critic method for optimizing parameters to maximize return.