After the recent buzz around DeepSeek’s approach to training their models with reinforcement learning, I decided to step back and break down the fundamentals of reinforcement learning. I wrote an intuitive blog post explaining it, containing the following topics:
<a href="https://open.substack.com/pub/diamantai/p/reinforcement-learning-explained?r=336pe4&amp;utm_campaign=post&amp;utm_medium=web&amp;showWelcomeOnShare=false" target="_blank" rel="noopener nofollow">Link to the blog post</a>
<ul>
<li>
Agents &amp; Environment: Where an AI learns by directly interacting with its world, adapting through feedback.
</li>
<li>
Policy: The evolving strategy that guides an agent’s actions, much like a dynamic playbook.
</li>
<li>
Q-Learning: A method that keeps a running estimate of how “good” each action is, driving the agent toward better outcomes.
</li>
<li>
Exploration-Exploitation Dilemma: The balancing act between trying new things and sticking to proven successes.
</li>
<li>
Function Approximation &amp; Memory: Techniques (often with neural networks and attention) that help RL systems generalize from limited experiences.
</li>
<li>
Hierarchical Methods: Breaking down large tasks into smaller, manageable chunks to build complex skills incrementally.
</li>
<li>
Meta-Learning: Teaching AIs how to learn more efficiently, rather than just solving a single problem.
</li>
<li>
Multi-Agent Setups: Situations where multiple AIs coordinate (or compete), each learning to adapt in a shared environment.
hope you’ll like it :)
</li>
</ul>

Stay at the forefront of AI with full access to my newsletters on cutting-edge techniques, exclusive tutorials, and extensive archives. Keep up-to-date with our open-source projects: RAG Techniques, GenAI Agents, and Prompt Engineering, and more

DiamantAI

Reinforcement learning involves teaching an AI to adapt and learn by interacting with its environment. Key topics include agents & environment, policy, Q-learning, the exploration-exploitation dilemma, function approximation & memory, hierarchical methods, meta-learning, and multi-agent setups.

Reinforcement Learning Explained