reinforcement learning:suitable problems - chunhualiao/public-docs GitHub Wiki

reinforcement learning

Checklist for Determining if a Problem is a Good Fit for Reinforcement Learning (RL)

✅ Positive Indicators (Good Fit for RL)

  1. Sequential Decision Making – The problem requires making a sequence of decisions over time.
  2. Trial-and-Error Learning – The solution benefits from an agent exploring actions and learning from feedback.
  3. Delayed Rewards – Actions have consequences that may not be immediately apparent, requiring long-term optimization.
  4. Stochastic or Complex Environments – The environment is unpredictable or difficult to model explicitly.
  5. No Clear Analytical Solution – Traditional optimization, rule-based, or supervised learning approaches are ineffective.
  6. Partial Observability – The agent does not have full information about the environment but must act optimally.
  7. Multi-Step Planning – The problem involves strategizing over multiple steps rather than a single-step decision.

❌ Negative Indicators (Bad Fit for RL)

  1. Single-Step Decisions – If each decision is independent of past actions, supervised learning or heuristic approaches might be better.
  2. Deterministic and Well-Defined Solutions – If an exact mathematical or algorithmic solution exists, RL is unnecessary.
  3. Data Efficiency is Crucial – RL often requires a vast amount of trial-and-error data, making it unsuitable for data-scarce problems.
  4. High Cost of Exploration – If making mistakes is too costly (e.g., medical surgeries, real-world safety-critical systems), RL may be impractical.
  5. No Clear Reward Function – If rewards are difficult to define or evaluate consistently, supervised learning may be more suitable.

Examples of Problems That Are a Good Fit for RL

✅ Positive Examples

  1. Game Playing (Chess, Go, StarCraft, Poker) – Sequential decisions, delayed rewards, and strategic planning.
  2. Robotics (Locomotion, Manipulation, Self-Driving Cars) – Requires learning from interactions in dynamic environments.
  3. Autonomous Trading Bots – Must make a sequence of trades with delayed rewards and uncertain future prices.
  4. Traffic Signal Optimization – Sequential decisions affecting congestion over time with noisy, dynamic environments.
  5. Healthcare Treatment Planning – Adaptive treatment strategies where effects unfold over time.

Examples of Problems That Are Not a Good Fit for RL

❌ Negative Examples

  1. Spam Email Classification – A single-step classification problem better suited for supervised learning.
  2. Handwriting Recognition – No sequential decision-making, best solved with deep learning or CNNs.
  3. Sorting Algorithms – A deterministic problem with well-defined mathematical solutions.
  4. Face Recognition – No trial-and-error learning needed; supervised learning is more effective.
  5. Simple A/B Testing – One-step decisions with clear rewards, better solved via statistical methods.

Would you like help evaluating a specific problem for RL suitability?