DRL Toolbox Matlab - Computing Notes

“Reinforcement Learning Toolbox,” Mathworks.com, 2026. https://au.mathworks.com/products/reinforcement-learning.html?requestedDomain= (accessed Feb 23, 2026).

Implementing Deep Reinforcement Learning Toolbox

Defining Observation and Action Spaces

The first stage of development involves establishing the operational boundaries. Data specifications must be defined for both observations (system states) and actions (available commands).

rlNumericSpec([5 1]); % Define continuous state space
rlFiniteSetSpec(1:4); % Define discrete action set

Configuring Function Approximators

Function approximators, such as Critics or Actors, serve as the internal processing unit. These components map environmental feedback to expected values or specific policy outcomes.

rlVectorQValueFunction(dnn_model, obsInfo, actInfo);
% Integrates the Deep Neural Network with defined spaces

Agent Initialization

The agent acts as the primary controller, utilizing algorithms like DQN or PPO. It governs the balance between exploring unknown states and exploiting known high-reward paths.

agentOpts = rlDQNAgentOptions(…);
% Define hyperparameters: Discount Factor, Sample Time, etc.

agent = rlDQNAgent(critic, agentOpts);
% Finalize agent assembly

Environment Integration

The simulation environment provides the necessary physics and logic. Function handles are typically used to link custom reset and step dynamics into the RL training loop.

env = rlFunctionEnv(obsInfo, actInfo, @stepFcn, @resetFcn);
% Connects state transition logic to the RL framework

Training and Deployment

The final phase involves an iterative training process to optimize the underlying networks, followed by the deployment of the trained policy for real-time decision making.

% Training Execution
stats = train(agent, env, trainOpts);

% Real-time Inference
action = getAction(agent, {currentObs});

Key Implementation Principle: Effective reinforcement learning relies on precise reward shaping to ensure the agent’s mathematical objectives align with intended system performance.