Environment for Q-Learning

I have recently begun working with the Reinforcement Learning Toolbox in MATLAB, and I am particularly interested in doing Q-Learning. I have taken a look at many of the examples available. What I settled on was to create an MDP environment with rlMDPEnv and use it for the Q-Learning, and the MDP object would be created with createMDP. However, in the example shown in https://www.mathworks.com/help/reinforcement-learning/ref/createmdp.html, the state-transition and reward matrices are manually populated. There are two issues I have with that:

1) My problem has so many states and actions that manually defining the state transitions and rewards for each situation would be way too tedious.

2) I thought Q-Learning bypasses the need to define state-transition probabilities. In fact, I thought that it was one of Q-Learning's main benefits. I understand that I am defining the state transitions for the MDP object and not at the Q-Learning level, but I still hope that I won't have to define the transition probabilities.

Does anyone know a solution for issue 1 and/or 2?

Thanks!

Answer 1

[Shubham]

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:

1. Define a custom environment by implementing the necessary functions (step, reset, etc.) that simulate the dynamics of your environment.
2. 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
    % Define properties (states, actions, etc.)
    
    methods
        function this = MyEnvironment()
            % Constructor to initialize your environment
        end
        
        function [Observation, Reward, IsDone, LoggedSignals] = step(this, Action)
            % Implement the logic for one step in your environment
            % based on the action. Return the next observation,
            % reward, and a flag indicating if the episode is done.
        end
        
        function InitialObservation = reset(this)
            % Reset the environment to an initial state and return the
            % initial observation.
        end
    end
end

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!