... for Learning COntrol (PILCO) and deep deterministic policy gradient (DDPG) reinforcement learning (RL) algorithms. Both rear-wheel-drive (RWD) and all-wheel-drive (AWD) results were compared, showing the capability of the agents to achieve escape from a ditch while minimizing wheel-slip for several ditch...

...Xinglin Yu; Yuhu Wu; Xi-Ming Sun; Wenya Zhou Balancing the exploration and exploitation in reinforcement learning is a commonly dilemma and time-wasting work. In this paper, a novel exploration policy used in Q-Learning, called Memory-greedy policy, is proposed to accelerate learning. By memory...