Episodic Memory-Double Actor-Critic Twin Delayed Deep Deterministic Policy Gradient.

Existing deep reinforcement learning (DRL) algorithms suffer from the problem of low sample efficiency. Episodic memory allows DRL algorithms to remember and use past experiences with high return, thereby improving sample efficiency. However, due to the high dimensionality of the state-action space in continuous action tasks, previous methods in continuous action tasks often only utilize the information stored in episodic memory, rather than directly employing episodic memory for action selection as done in discrete action tasks.