Chapter 7 - Hard

Multi-Agent Reinforcement Learning (MARL)

• MARL combines reinforcement learning with fields such as game theory, economics, and multi-agent systems.

Key Concepts

• Nash Equilibrium: A situation where no agent can benefit by changing its strategy while others keep theirs unchanged.
• Pareto Efficiency: An allocation where no agent can be made better off without making another worse off.
• Stochastic Games: Games with probabilistic transitions between states, requiring agents to plan over an uncertain future.
• Extensive-Form Games: Represented by a game tree, showing the sequential nature of decisions and information available at each decision point.

Strategies

• Competitive Strategies: Agents aim to maximize their individual rewards, often at the expense of others (e.g., Chess).
• Cooperative Strategies: Agents work together to achieve a common goal (e.g., Collaborative robotics).
• Mixed Strategies: Agents may exhibit both competitive and cooperative behaviors (e.g., Capture the Flag).

Competitive Behavior

• Competitive Behavior: Strategies where agents aim to outperform each other, often leading to adversarial relationships.
• Examples: bluffing, deception, and counter-strategies.

Cooperative Behavior

• Cooperative Behavior: Strategies where agents coordinate their actions to achieve a common objective.
• Examples: sharing information, planning joint actions, and aligning goals.

Multi-Objective Reinforcement Learning

• Multi-Objective Reinforcement Learning: Optimizing multiple objectives simultaneously, often requiring trade-offs between competing goals.
• Counterfactual Regret Minimization (CFR): An algorithm for decision-making in games that minimizes regret by considering counterfactual scenarios.

Deep Counterfactual Regret Minimization (Deep CFR)

• Deep CFR: A variant of CFR that uses deep learning to handle large state and action spaces, enabling more complex decision-making.

Cooperative Behavior

• Centralized Training/Decentralized Execution: Training agents together with shared information but allowing them to act independently during execution.
• Opponent Modeling: Predicting and responding to the actions of other agents to improve strategic decision-making.
• Communication: Developing strategies that enable agents to share information and coordinate their actions effectively.

Mixed Behavior

• Evolutionary Algorithms: Optimization algorithms inspired by natural selection, used to evolve agent strategies over time.
• Swarm Computing: Algorithms inspired by the collective behavior of social animals, used for distributed problem-solving.
• Population-Based Training: Training multiple agents with different strategies and allowing them to evolve through a process similar to natural selection.

Multi-Agent Environments

• Poker: A competitive game where agents must balance bluffing and strategic decision-making to succeed against other players.
• Hide and Seek: A cooperative game where agents must work together to find optimal hiding or seeking strategies.
• Capture the Flag: A mixed game that combines competitive and cooperative elements, requiring teams to strategize to capture the opponent's flag while defending their own.