Reinforcement Learning Implementation using DQN

Hi All!

I want to implement a Reinforcement Learning program using DQN in Matlab R2013b. I have a defined state and action space. I have generated the database of all the necessary information that will help in defining the state space. I have created an initial state but i am unable to use that state to train the agent for implementing my problem. I will be oblidged if someone could guide me in starting the training process of the agent.

Looking forward to your reply.

A. Ahmed

Answer 1

[Yash Sharma]

To implement a Deep Q-Network (DQN) agent for reinforcement learning in the latest versions of MATLAB, you can leverage the Reinforcement Learning Toolbox. This toolbox simplifies the process by providing predefined environments, agents, and training functions. Here's a concise guide to get started:

1. Define the Environment

First, define your environment, which includes the state and action spaces. If you're working with a custom environment, you'll need to create it by subclassing the rl.env.MATLABEnvironment class or using the rlFunctionEnv function if your environment can be described as a set of MATLAB functions.

observationInfo = rlNumericSpec([numObservations 1]);
actionInfo = rlFiniteSetSpec(actions);
env = rlFunctionEnv(observationInfo,actionInfo,"myStepFunction","myResetFunction");

2. Create the DQN Agent

To create a DQN agent, you need to define a deep neural network that takes the environment's observation as input and outputs the action value function Q(s,a). Use the Deep Learning Toolbox to create this network.

layers = [
    featureInputLayer(numObservations,'Normalization','none','Name','state')
    fullyConnectedLayer(24,'Name','fc1')
    reluLayer('Name','relu1')
    fullyConnectedLayer(length(actions),'Name','fc2')];
lgraph = layerGraph(layers);

criticOpts = rlRepresentationOptions('LearnRate',1e-03,'GradientThreshold',1);
critic = rlQValueRepresentation(lgraph,observationInfo,actionInfo,...
    'Observation',{'state'},'Action',{'fc2'},criticOpts);

agentOpts = rlDQNAgentOptions(...
    'UseDoubleDQN',false, ...  
    'TargetSmoothFactor',1e-3, ...
    'ExperienceBufferLength',1e6, ...
    'DiscountFactor',0.99, ...
    'MiniBatchSize',256);
agent = rlDQNAgent(critic,agentOpts);

3. Train the Agent

Use the train function to train your agent. Define training options including the maximum number of episodes, the maximum steps per episode, and other parameters to control the training process.

trainingOpts = rlTrainingOptions(...
    'MaxEpisodes',1000,...
    'MaxStepsPerEpisode',500,...
    'Verbose',false,...
    'Plots','training-progress',...
    'StopTrainingCriteria','AverageReward',...
    'StopTrainingValue',500);

trainingStats = train(agent,env,trainingOpts);

Find attached below relevant documentation

Deep Q-Network (DQN) Agent: https://www.mathworks.com/help/reinforcement-learning/ug/dqn-agents.html

Hope it helps!