Hi Avinash,
You've raised two very relevant points regarding the use of Q-Learning, especially in the context of environments with a large number of states and actions, and the nature of Q-Learning itself. Let's address each issue separately:
Issue 1: Large State and Action Spaces
For problems with a large number of states and actions, manually defining the state-transition and reward matrices is indeed impractical. Here are a few strategies to handle this:
- Instead of using a tabular approach, where you have a discrete entry for every state-action pair, consider using function approximation techniques. Deep Q-Networks (DQNs) are a popular choice for approximating the Q-value function using neural networks. This way, you don't need to manually define transitions for every possible state-action pair.
- Q-Learning is a model-free reinforcement learning algorithm, meaning it can learn the optimal policy directly from interactions with the environment without needing a model of the environment (i.e., the state-transition probabilities). For environments where it's impractical to define all transitions, you can implement a simulation of the environment that, given a current state and an action, returns the next state and the reward. This simulation can be as simple or complex as necessary, based on the dynamics of your problem.
Issue 2: Bypassing the Need for State-Transition Probabilities in Q-Learning
You're correct in noting that one of the advantages of Q-Learning is that it does not require knowledge of the state-transition probabilities. Q-Learning learns the value of state-action pairs (Q-values) based on the rewards observed through interacting with the environment. This property makes Q-Learning particularly useful for problems where the state-transition probabilities are unknown or difficult to model.
To address both issues in the context of using MATLAB's Reinforcement Learning Toolbox:
- Instead of defining a static MDP model with createMDP, you might want to simulate your environment. You can create a custom environment in MATLAB that defines the rules, actions, and rewards dynamically as the agent interacts with it. This approach is more flexible and scalable for complex problems.
- Custom Environment for Q-Learning: To implement Q-Learning in such cases, you would:
- Define a custom environment by implementing the necessary functions (step, reset, etc.) that simulate the dynamics of your environment.
- Use this environment with the Q-Learning algorithm provided by MATLAB or implement your own Q-Learning logic if you're working with specific requirements.
Here's a simplified structure for creating a custom environment:
classdef MyEnvironment < rl.env.MATLABEnvironment
function this = MyEnvironment()
function [Observation, Reward, IsDone, LoggedSignals] = step(this, Action)
function InitialObservation = reset(this)
By creating a custom environment, you can simulate the dynamics of your system without manually defining all state transitions and rewards, and then apply Q-Learning or any other suitable RL algorithm.
I hope this helps!