41![LETTER doi:nature14236 Human-level control through deep reinforcement learning LETTER doi:nature14236 Human-level control through deep reinforcement learning](https://www.pdfsearch.io/img/b89c42dfa0632faf36a1e3bd43a835d1.jpg) | Add to Reading ListSource URL: storage.googleapis.comLanguage: English - Date: 2016-09-12 10:19:54
|
---|
42![Reinforcement learning and causal models Samuel J. Gershman Department of Psychology and Center for Brain Science Harvard University December 6, 2015 Reinforcement learning and causal models Samuel J. Gershman Department of Psychology and Center for Brain Science Harvard University December 6, 2015](https://www.pdfsearch.io/img/56ea926362af11b7b4c8c8f1b4cfb85a.jpg) | Add to Reading ListSource URL: gershmanlab.webfactional.comLanguage: English - Date: 2015-12-09 15:50:50
|
---|
43![OFFER: Off-Environment Reinforcement Learning Kamil Ciosek Shimon Whiteson Department of Computer Science, University of Oxford OFFER: Off-Environment Reinforcement Learning Kamil Ciosek Shimon Whiteson Department of Computer Science, University of Oxford](https://www.pdfsearch.io/img/80bcffa9c500386ad61f6d68a94edd3e.jpg) | Add to Reading ListSource URL: www.ciosek.netLanguage: English |
---|
44![GridFormation: Towards Self-Driven Online Data Partitioning using Reinforcement Learning Gabriel Campero Durand Marcus Pinnecke GridFormation: Towards Self-Driven Online Data Partitioning using Reinforcement Learning Gabriel Campero Durand Marcus Pinnecke](https://www.pdfsearch.io/img/7eadc34efe5fad26e450afbe7861159d.jpg) | Add to Reading ListSource URL: wwwiti.cs.uni-magdeburg.deLanguage: English - Date: 2018-05-02 12:45:12
|
---|
45![LETTER Communicated by Andrew Barto Robust Reinforcement Learning Jun Morimoto LETTER Communicated by Andrew Barto Robust Reinforcement Learning Jun Morimoto](https://www.pdfsearch.io/img/cc27f7e84d9b5f968ae1bf9c843fcd65.jpg) | Add to Reading ListSource URL: www.cns.atr.jpLanguage: English - Date: 2016-12-10 01:09:35
|
---|
46![Deep Reinforcement Learning in Large Discrete Action Spaces Gabriel Dulac-Arnold*, Richard Evans*, Hado van Hasselt, Peter Sunehag, Timothy Lillicrap, Jonathan Hunt, Timothy Mann, Theophane Weber, Thomas Degris, Ben Cop Deep Reinforcement Learning in Large Discrete Action Spaces Gabriel Dulac-Arnold*, Richard Evans*, Hado van Hasselt, Peter Sunehag, Timothy Lillicrap, Jonathan Hunt, Timothy Mann, Theophane Weber, Thomas Degris, Ben Cop](https://www.pdfsearch.io/img/7f0fce02d2893b00e115896eaf7b01d1.jpg) | Add to Reading ListSource URL: arxiv.orgLanguage: English - Date: 2016-04-04 21:48:24
|
---|
47![Intensional data management Reinforcement learning Applications Intensional data management Reinforcement learning Applications](https://www.pdfsearch.io/img/a186525dd0ee35c16579f9c8091e22a8.jpg) | Add to Reading ListSource URL: pierre.senellart.comLanguage: English - Date: 2018-03-09 02:06:18
|
---|
48![Distributed Computing Prof. R. Wattenhofer Topics in Deep Reinforcement Learning In 2015 Google Deepmind published their DQN paper in which they present Distributed Computing Prof. R. Wattenhofer Topics in Deep Reinforcement Learning In 2015 Google Deepmind published their DQN paper in which they present](https://www.pdfsearch.io/img/c8cab4e0217c2da37a8e01f92f719690.jpg) | Add to Reading ListSource URL: www.tik.ee.ethz.chLanguage: English - Date: 2018-05-04 09:19:05
|
---|
49![Risk-Constrained Reinforcement Learning with Percentile Risk Criteria Yinlam Chow YCHOW @ STANFORD . EDU Risk-Constrained Reinforcement Learning with Percentile Risk Criteria Yinlam Chow YCHOW @ STANFORD . EDU](https://www.pdfsearch.io/img/e8afdb04331296e92e42d87e8c289b54.jpg) | Add to Reading ListSource URL: lucasjanson.rc.fas.harvard.eduLanguage: English - Date: 2017-09-29 11:35:53
|
---|
50![Constrained Policy Optimization Joshua Achiam 1 David Held 1 Aviv Tamar 1 Pieter Abbeel 1 2 Abstract For many applications of reinforcement learning it can be more convenient to specify both a reward function and constra Constrained Policy Optimization Joshua Achiam 1 David Held 1 Aviv Tamar 1 Pieter Abbeel 1 2 Abstract For many applications of reinforcement learning it can be more convenient to specify both a reward function and constra](https://www.pdfsearch.io/img/b5ce07e94d8ff665e8ce6045b6f7391f.jpg) | Add to Reading ListSource URL: proceedings.mlr.press- Date: 2018-02-06 15:06:57
|
---|