31.08.2016

European Workshop on Reinforcement Learning (EWRL 2016)

Want to enjoy a two-day workshop on Reinforcement Learning, without the stress of the many NIPS parallel sessions? Look no further!

1. Paper Submission

We invite submissions from the entire reinforcement learning spectrum.  Authors can submit a 2-6 pages paper in JMLR format (excluding references) that will be reviewed by the program committee in a double-blind procedure. The papers can present new work or give a summary of recent work of the author(s). All papers will be considered for the poster sessions. Outstanding long papers (4-6 pages) will also be considered for a 20 minutes oral presentation. Accepted papers are going to be published in an arxiv.org collection.

Submission deadline: 16/09/2016

Notification: 04/10/2016

Page limit: 2-6 pages excluding references.

Paper format: JMLR format, anonymous.

Submission website: https://easychair.org/conferences/?conf=ewrl201

2. Description

The 13th European workshop on reinforcement learning (EWRL 2016) invites reinforcement-learning researchers to participate in the newest edition of this world class event. We plan to make this an exciting meeting for researchers worldwide, not only for the presentation of top quality papers, but also as a forum for ample discussion of open problems and future research directions. EWRL 2016 will consist of 11+ invited talks, contributed paper presentations, discussion sessions spread over a two day period, and a poster session.

Reinforcement learning is an active field of research which deals with the problem of sequential decision making in unknown (and often) stochastic and/or partially observable environments. Recently there has been a wealth of both impressive empirical results, as well as significant theoretical advances. Both types of advances are of significant importance and we would like to create a forum to discuss such interesting results.

The workshop will cover a range of sub-topics including (but not limited to):

- Exploration/Exploitation and multi-armed bandits

- Deep RL

- Representation learning for RL

- Large-scale RL

- Theoretical aspects of RL

- Policy search and actor-critic methods

- Online learning algorithms

- RL in non-stationary environments

- Risk-sensitive RL

- Transfer and Multi-task RL

- Empirical evaluations in RL

- Kernel methods for RL

- RL in partially observable environments

- Imitation learning and Inverse RL

- Bayesian RL

- Multi agent RL

- Applications of RL

- Open problems

3. Organizing Committee

Gergely Neu

Vicenç Gómez

Csaba Szepesvári

 

For more information, see https://ewrl.wordpress.com/ewrl13-2016/