Faster Deep Q-learning using Neural Episodic Control
This work addresses sample efficiency in deep reinforcement learning for agents, but it is incremental as it builds on existing methods like NEC and DQN.
The paper tackles the problem of slow learning in deep Q-learning by combining Neural Episodic Control (NEC) with DQN to improve initial learning speed, achieving faster learning than Double DQN or N-step DQN in Pong experiments.
The research on deep reinforcement learning which estimates Q-value by deep learning has been attracted the interest of researchers recently. In deep reinforcement learning, it is important to efficiently learn the experiences that an agent has collected by exploring environment. We propose NEC2DQN that improves learning speed of a poor sample efficiency algorithm such as DQN by using good one such as NEC at the beginning of learning. We show it is able to learn faster than Double DQN or N-step DQN in the experiments of Pong.