Decision-Making in AI: Reinforcement Learning, MDPs, and Game Theory

Authored via NotebookLM

Ever wondered how an AI learns to play a perfect game of chess or navigate a complex grid? This guide breaks down the transition from single-agent logic to the high-stakes world of multi-agent conflict and cooperation.

1. Modeling the World: Markov Decision Processes (MDPs)
Before an agent can act, it needs a map of the "rules." We define the universe through:
- The Framework: Defining environments via States, Actions, Transition Models (the "physics" of the world), and Reward Signals.
- The Bellman Equation: The mathematical heartbeat of RL that allows for Value Iteration and Policy Iteration to find the absolute best strategy for long-term gain.

2. Learning from Experience: Reinforcement Learning
When the rules are unknown, agents must learn by doing.
- Q-Learning: We explore how agents calculate the utility of their actions based on raw experience rather than a pre-written manual.
- The Dilemma: Navigating the Exploration-Exploitation trade-off—deciding when to try something new versus when to stick with a known win.

3. The Math of Conflict: Game Theory
- How do machines behave when they aren't alone? We shift from solo puzzles to multi-agent competition.
- Zero-Sum Games: Understanding perfect-information scenarios where one player's gain is another's loss.
- Tactical Search: How Minimax and Alpha-beta pruning allow AI to "look ahead" and anticipate an opponent’s optimal response.

4. Cooperation & Strategy: The Prisoner’s Dilemma
- In a world of hidden information, cooperation is a calculated risk.
- The Nash Equilibrium: Analyzing the state where no player can benefit by changing their strategy alone.
- Evolution of Trust: Using the Iterated Prisoner’s Dilemma and the Folk Theorem to see how strategies like "Tit-for-Tat" encourage long-term cooperation.

5. State-of-the-Art: Rainbow DQN
- We bridge the gap between classic theory and modern Deep Learning.
= The Atari Breakthrough: A look at the Rainbow architecture, a powerhouse "super-algorithm" that combines six major RL enhancements—including Double Q-learning, Dueling Networks, and Prioritized Experience Replay—to achieve superhuman performance.

Preparation for OMSCS CS 7641 Final Exam

Видео Decision-Making in AI: Reinforcement Learning, MDPs, and Game Theory канала Jesse Arzate