The project trains a deep reinforcement learning agent that controls a truck in a simulation game using only W, A, S, D inputs. It combines a CNN-based embedder pretrained via semi-supervised learning with a Dueling Network architecture and LSTM-based future state prediction, following Deep Successor RL principles. The model learns to predict value and advantage separately and anticipates future states across multiple timesteps to accelerate learning.