Vidura Bandara
Wijekoon

Workshop description

I'm thrilled to share insights from a pivotal reinforcement learning session I recently conducted, marking yet another milestone in my journey of empowering minds with AI knowledge. 🚀 During this session, we explored the fundamentals of reinforcement learning, starting with the concept of an agent—an entity that interacts with its environment to achieve specific goals. We delved into the Markov Decision Process (MDP), a mathematical framework essential for modeling decision-making under uncertainty, and the Bellman Equation, which simplifies complex decision problems into manageable subproblems. We also covered Q-learning, a model-free algorithm that learns the value of action-state pairs, guiding agents to select the best actions in any given state. Deep Q-Networks (DQNs) were another highlight, showing how deep neural networks can approximate Q-values, making it possible to tackle high-dimensional state spaces. Finally, we touched on Decision-Process Optimization (DPO), which refines strategies to enhance performance in complex systems. A special thanks to all participants for their enthusiasm and engagement. It was a pleasure to share this knowledge, and I look forward to our continued journey in mastering AI-driven solutions. 🔗 Access the session recording here: https://lnkd.in/g8xDsRkb Here's to the power of learning and the endless possibilities of AI! 🌟