Q‐table - chunhualiao/public-docs GitHub Wiki

While there's a vast landscape of reinforcement learning algorithms, only a few are commonly used in practice for learning a Q-table. Here's a breakdown:

Core Algorithms:

  • Q-Learning: This is the most fundamental and widely-used algorithm for Q-table learning. It's an off-policy algorithm, meaning it learns the optimal policy independent of the agent's current behavior.

    • Variant: Deep Q-Networks (DQN) extend Q-learning by using a neural network to approximate the Q-function, capable of handling high dimensional state-spaces.

  • SARSA (State-Action-Reward-State-Action): This is an on-policy algorithm. Unlike Q-learning, SARSA learns the value of the policy it's currently following. This makes a difference in highly stochastic scenarios, where the specific actions taken can impact the learning.

  • Expected SARSA: It is another model-free reinforcement learning algorithm for Q-learning. It's an upgraded version of SARSA, and it can make use of more information.

Variations and Extensions (often combined with the core algorithms):

  • Experience Replay: Often used in DQN and other neural network-based algorithms to improve the stability and efficiency of learning. It stores past experiences, which can be sampled randomly to break the correlations in the data seen during learning.
  • Double Q-Learning: Attempts to reduce overestimation bias, which can be a problem in Q-learning. It uses two sets of Q-values to decouple the selection and evaluation of actions.
  • Prioritized Experience Replay: Extends experience replay by prioritizing the storage and sampling of experiences based on the magnitude of their TD error (how surprising the experience was).

So, in practice, the most important and frequently used algorithms for directly updating a Q-table are Q-Learning and SARSA, with Q-Learning being the dominant choice, often enhanced with techniques like experience replay when a neural network is used to approximate the table values.

Important Considerations:

  • Choosing the right algorithm depends a lot on the characteristics of the environment (deterministic vs. stochastic, continuous vs. discrete state/action spaces), the desired behavior (on-policy vs. off-policy), and the computational resources available.
  • While other algorithms can be used to optimize the learning process (e.g., optimizing exploration/exploitation), they typically build upon the core Q-learning or SARSA framework. In many practical usages, the differences come down to choices about the training procedure (i.e., how you sample and update the Q-table) and the exploration/exploitation strategy (how you balance choosing random actions vs. choosing the best action based on the table)
  • It's worth being aware that other approaches, such as policy gradients, value-function-free methods and model-based reinforcement learning, exist, but don't directly maintain a Q-table; or do not use them (directly) to create the final policy.

In summary, while research in reinforcement learning is very active, in practice, the number of primary algorithms used to directly learn a Q-table is quite small. It's usually Q-Learning or SARSA, often with modern techniques like experience replay and potentially Double Q-Learning/variants, or sometimes Expected SARSA. The choice comes down to the specific problem and the trade-offs between computational cost, stability, and exploration/exploitation behavior.