Focuses on the use of deep reinforcement learning (RL) for game simulation in the context of car racing implementing DDQN.
We apply the deep double Q-learning (DDQN) algorithm to train an agent to drive a car in a 2D racing game. We develop an RL-based model that can learn to play a racing game by observing the game state and taking appropriate actions based on the rewards received. Our model uses a neural network to learn the Q-value function and optimize the agent’s policy.
We evaluate the performance of our model and compare it to other approaches using metrics such as the average reward and success rate achieved by the agent. Our results show that our DDQN-based approach outperforms the other methods and can successfully learn to play the racing game.