Simulink Controls a Vehicle in VR Environment with Reinforcement Learning Implementation

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Resource Overview

Simulink controls a vehicle in a VR environment equipped with 5 distance sensors. The vehicle gradually learns to avoid walls and obstacles using Q-learning reinforcement learning algorithm with neural network-based Q-function approximation. Implementation includes simulated annealing for exploration strategy, resulting in initial frequent collisions during training phase that significantly reduce after approximately 10 learning iterations. The 3D vehicle model utilizes the VR model originally published by "w198406141" in the virtual reality section of this forum, with integration through Simulink 3D Animation toolbox and custom S-function blocks for sensor data processing and control logic.

Detailed Documentation

Simulink can be employed to control a vehicle within a VR environment. This vehicle is equipped with 5 distance sensors that enable it to progressively learn collision avoidance behavior against walls and obstacles. The implementation utilizes Q-learning reinforcement learning algorithm where the Q-function is approximated through a neural network architecture, typically implemented using MATLAB's Neural Network Toolbox or custom neural network blocks. During the initial training phase, the vehicle may frequently collide with obstacles due to the incorporation of simulated annealing for exploration strategy, which balances exploration and exploitation. However, after approximately 10 learning iterations, the vehicle demonstrates significant improvement and rarely collides. The 3D visualization utilizes the VR model published by "w198406141" in the virtual reality section of this forum, integrated through Simulink's VR Sink block and configured with proper coordinate transformations and sensor positioning parameters.

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