Continuous Episodic Control

Abstract--Non-parametric episodic memory can be used to quickly latch onto high-rewarded experience in reinforcement learning tasks. In contrast to parametric deep reinforcement learning approaches in which reward signals need to be backpropagated slowly, these methods only need to discover the solution once, and may then repeatedly solve the task. However, episodic control solutions are stored in discrete tables, and this approach has so far only been applied to discrete action space problems. Deep reinforcement learning (RL) methods have recently Episodic memory is a term that originates from neuroscience demonstrated superhuman performance on a wide range of [9], where it refers to memory that we can quickly tasks, including Gran Turisma [1], StarCraft [2], Go [3], etc. recollect. In the context of RL, this concept has generally been However, in these methods, the weights of neural networks are implemented as a non-parametric (or semi-parametric) table slowly updated over time to match the target predictions based that can be read from and written into rapidly. Information on the encountered reward signal.