Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Next revision
Previous revision
Last revision Both sides next revision
replay [2017/07/08 13:55]
127.0.0.1 external edit
replay [2018/10/02 20:22]
admin
Line 9: Line 9:
 Dealing with sparse rewards is one of the biggest challenges in Reinforcement Learning (RL). We present a novel technique called Hindsight Experience Replay which allows sample-efficient learning from rewards which are sparse and binary and therefore avoid the need for complicated reward engineering. It can be combined with an arbitrary off-policy RL algorithm and may be seen as a form of implicit curriculum. ​ Dealing with sparse rewards is one of the biggest challenges in Reinforcement Learning (RL). We present a novel technique called Hindsight Experience Replay which allows sample-efficient learning from rewards which are sparse and binary and therefore avoid the need for complicated reward engineering. It can be combined with an arbitrary off-policy RL algorithm and may be seen as a form of implicit curriculum. ​
  
 +https://​openreview.net/​forum?​id=r1lyTjAqYX Recurrent Experience Replay in Distributed Reinforcement Learning ​
  
 +We investigate the effects of parameter lag resulting in representational drift and recurrent state staleness and empirically derive an improved training strategy. ​
 +
 +https://​arxiv.org/​abs/​1809.10635v1 Generative replay with feedback connections as a general strategy for continual learning