Reinforcement Learning Strategies Quiz

Reminders

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Reinforcement Learning

• A field that combines psychological learning theories (like classical conditioning) and control theory (from mechanical engineering).
• Useful for understanding agents that make repeated decisions in an environment to achieve goals.
• Algorithms are applicable to AI systems and explain human/animal behaviour.

Problem Solving

• In cognitive science, "solving a problem" means identifying a goal/reward state and taking steps to achieve it.
• Problem-solving often involves multiple steps and figuring out the right next step.
• Actions can either rely on previous experience ("model-free") or a multi-step plan ("model-based").

Learning Strategies

• Unsupervised learning: Identifying patterns in the world without a predetermined goal.
• Supervised learning: Learning the correct response to a stimulus.

Q-Learning

• Q-value represents the quality of an action in a specific state. It is the sum of expected future rewards.
• Learning Q-values involves observing past experiences to predict future outcomes of actions.
• The highest quality action is chosen based on the calculated Q-values.

Tic-Tac-Toe Example

• Understanding Q-values in a game involves determining the probability of winning after a specific action.
• Q-value for playing X in a particular position (e.g., top-left corner) is calculated based on past win/loss records from that starting position.

Chess Example

• Analyzing a board position involves determining which action (move) has a higher associated Q-value based on past observations/simulations.

Model-Free Learning

• Learning Q-values purely from experience, without a model of how actions affect the environment.
• It doesn't require a model of how actions change states.
• Quickly makes decisions.

Model-Based Learning

• Using a model of the environment to predict the effects of potential actions.
• It creates a plan outlining the actions for achieving a goal.
• Adapts to environmental changes.

Combining Model-Free and Model-Based Methods

• Many real-world AI systems combine model-free and model-based algorithms to leverage the strengths of both.

Real-World Problems

• Real-world applications often involve complex state spaces and continuous actions.
• Rewards might be far in the future.
• Learning models may require extremely large numbers of attempts (or "training").

Expertise in Problem-Solving

• Experts identify the most important aspects of a state.
• Estimate expected Q (quality) without simulating future possibilities of actions.
• Rely on pre-learned, automatic action sequences, rather than conscious decision-making.

Examples of Learning Agents

• AlphaGo: The first program to master Go using neural networks and tree search.
• AlphaGo Zero: Learned to play Go without any human knowledge.
• AlphaZero: Masters perfect-information games using a single algorithm.
• MuZero: Learns game rules and applies knowledge to unknown environments.

Summary of Reinforcement Learning

• The framework of reinforcement learning describes a variety of strategies.
  • Model-free strategies use stored knowledge of actions contributing to goals.
  • Model-based strategies create explicit action plans to achieve goals.

Test your knowledge on the different strategies in reinforcement learning, focusing on model-free and model-based approaches. This quiz covers key concepts such as Q values, decision-making, and challenges in large state spaces. Dive into the intricacies of how experts navigate through unfamiliar situations in this domain.