PROGRAM

8:20 AM	Welcome Özgür Şimşek & George Konidaris
	Invited talk: High-level actions (watch) Stuart Russell
9:30 AM	Paper presentations
	Manifold embeddings for model-based reinforcement learning of neurostimulation policies (watch) Keith Bush, Joelle Pineau and Massimo Avoli
	Dyna(k): A multi-step Dyna planning (watch) Hengshuai Yao, Rich Sutton, Shalabh Bhatnagar, Dongcui Diao and Csaba Szepesvári
	An empirical comparison of abstraction in models of Markov decision processes (watch) Todd Hester and Peter Stone
	Towards feature selection in actor-critic algorithms Khashayar Rohanimanesh, Nicholas Roy and Russ Tedrake
10:30 AM	Coffee break
11:00 AM	Paper presentations
	Basis function construction for hierarchical reinforcement learning (watch) Sarah Osentoski and Sridhar Mahadevan
	Finding equivalences among abstract actions Alicia P. Wolfe
11:30 AM	Panel discussion: The future of abstraction in reinforcement learning Panelists: Andrew Barto, Tom Dietterich (slides), Michael Littman and Ron Parr
12:30 PM	Poster spotlights (and lunch)
	Improving Bayesian reinforcement learning using transition abstraction Daniel Acuna and Paul Schrater
	Situation dependent spatial abstraction in reinforcement learning based on structural knowledge (watch) Lutz Frommberger
	Automated discovery of options in factored reinforcement learning (watch) Olga Kozlova, Olivier Sigaud and Christophe Meyer
	Hierarchical skill learning for high-level planning (watch) James MacGlashan and Marie desJardins
1:30 PM	Invited talks by Rich Sutton, Wolfram Schultz, Gerald Tesauro and Andrew Ng at the Multidisciplinary Symposium on Reinforcement Learning (MSRL)
6:00 PM	Joint poster session with MSRL