Week 2
Hello everyone, and welcome to my week 2 updates of the senior research project. After spending my first week learning the fundamentals of reinforcement learning, this week I shifted my focus toward finding open-courseware and resources to start training my own RL model. The transition from theory to implementation has been exciting, as I explored various platforms and frameworks that provide environments for reinforcement learning experiments. Understanding how to structure an RL model, define states and actions, and set up reward mechanisms has been a key part of my learning process.
Alongside this, I also delved into the mathematical foundations necessary for working with reinforcement learning algorithms, particularly Bellman’s Equation. This equation is fundamental in RL because it expresses the relationship between the value of a state and the expected rewards from future actions. By breaking down complex decision-making into recursive value functions, Bellman’s Equation helps optimize an agent’s long-term rewards. Understanding the mathematics behind it, including Markov Decision Processes (MDPs), dynamic programming, and value iteration, is crucial for building more efficient RL models.
This week has been all about bridging the gap between theoretical knowledge and practical implementation. As I continue my research, I am excited to begin experimenting with different algorithms and testing reinforcement learning models in various environments. Stay tuned for more updates as I make progress in developing and refining my own RL system!
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