Spike-Based Reinforcement Learning in Continuous State and Action Space: When Policy Gradient Methods Fail

Eleni Vasilaki; Nicolas Frémaux; Robert Urbanczik; Walter Senn; Wulfram Gerstner

Download: PDF | Citation | XML
EzReprint New & improved!

Published in the December 2009 Issue of PLoS Computational Biology

Metrics info

Average Rating (0 User Ratings)

- Currently 0/5 Stars.
See all categories
1. Insight
  - Currently 0/5 Stars.
2. Reliability
  - Currently 0/5 Stars.
3. Style
  - Currently 0/5 Stars.
Rate This Article

Rate This Article

Please follow our guidelines for rating and review our competing interests policy. Comments that do not conform to our guidelines will be promptly removed and the user account disabled. The following must be avoided:

Remarks that could be interpreted as allegations of misconduct
Unsupported assertions or statements
Inflammatory or insulting language

Compose Your Annotation

Insight

Reliability

Style

Enter your comment title Enter your comment

Declare any competing interests.

No, I don't have any competing interests to declare.
Yes, I have competing interests to declare (enter below):

Ratings can include the following markup tags:

Emphasis: ''italic'' '''bold''' '''''bold italic'''''

Other: ^^^superscript^^ ~~_subscript~~

Metrics info

Average Rating (0 User Ratings)

Related Content

Share this Article info

Spike-Based Reinforcement Learning in Continuous State and Action Space: When Policy Gradient Methods Fail

Related Articles info

Related Articles on the Web

Bookmarked in

Related Blog Posts info

Trackbacks

Rate This Article