We propose Ephemeral Value Adjusments (EVA): a means of allowing deep reinforcement learning agents to rapidly adapt to experience in their replay buffer. EVA shifts the value predicted by a neural network with an estimate of the value function found by prioritised sweeping over experience tuples from the replay buffer near the current state. EVA combines a number of recent ideas around combining episodic memory-like structures into reinforcement learning agents: slot-based storage, content-based retrieval, and memory-based planning. We show that EVA is performant on a demonstration task and Atari games.

We present a novel multi-agent RL approach, Selective Multi-Agent Prioritized Experience Relay, in which agents share with other agents a limited number of transitions they observe during training.

Exploring unknown environments efficiently is a fundamental challenge in unsupervised goal-conditioned reinforcement learning.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Acontinual learning agent learns online with anon-stationary andnever-ending stream ofdata. Thekeytosuch learning process istoovercome thecatastrophic forgetting of previously seen data, which is a well known problem of neuralnetworks.

This creates a tight feedback loop between representation learning,memory,reinforcementlearning,andsampling-basedplanning.

Recently, model-based reinforcement learning algorithms have demonstrated remarkable efficacy in visual input environments.