Ape-X

Introduced by Horgan et al. in Distributed Prioritized Experience Replay

Ape-X is a distributed architecture for deep reinforcement learning. The algorithm decouples acting from learning: the actors interact with their own instances of the environment by selecting actions according to a shared neural network, and accumulate the resulting experience in a shared experience replay memory; the learner replays samples of experience and updates the neural network. The architecture relies on prioritized experience replay to focus only on the most significant data generated by the actors.

In contrast to Gorila, Ape-X uses a shared, centralized replay memory, and instead of sampling uniformly, it prioritizes, to sample the most useful data more often. All communications are batched with the centralized replay, increasing the efficiency and throughput at the cost of some latency. And by learning off-policy, Ape-X has the ability to combine data from many distributed actors, by giving the different actors different exploration policies, broadening the diversity of the experience they jointly encounter.

Source: Distributed Prioritized Experience Replay

Latest Papers

PAPER DATE
Recurrent Distributed Reinforcement Learning for Partially Observable Robotic Assembly
Jieliang LuoHui Li
2020-10-15
Dynamic Experience Replay
Jieliang LuoHui Li
2020-03-04
Google Research Football: A Novel Reinforcement Learning Environment
| Karol KurachAnton RaichukPiotr StańczykMichał ZającOlivier BachemLasse EspeholtCarlos RiquelmeDamien VincentMarcin MichalskiOlivier BousquetSylvain Gelly
2019-07-25
Macro action selection with deep reinforcement learning in StarCraft
| Sijia XuHongyu KuangZhi ZhuangRenjie HuYang LiuHuyang Sun
2018-12-02
An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution
| Rosanne LiuJoel LehmanPiero MolinoFelipe Petroski SuchEric FrankAlex SergeevJason Yosinski
2018-07-09
Deep Curiosity Search: Intra-Life Exploration Can Improve Performance on Challenging Deep Reinforcement Learning Problems
Christopher StantonJeff Clune
2018-06-01
Distributed Prioritized Experience Replay
| Dan HorganJohn QuanDavid BuddenGabriel Barth-MaronMatteo HesselHado van HasseltDavid Silver
2018-03-02

Tasks

TASK PAPERS SHARE
Atari Games 2 28.57%
Game of Football 1 14.29%
Real-Time Strategy Games 1 14.29%
Starcraft 1 14.29%
Image Classification 1 14.29%
Montezuma's Revenge 1 14.29%

Categories