Time | Program |
---|---|
8h00 – 8h55 | Registration |
Session 1 | |
8h55 – 9h00 | Opening |
9h00 - 9h20 |
Case-based Policy Inference for Transfer in Reinforcement Learning R. Glatt, F. L. Silva and A. H. R. Costa |
9h20 – 9h40 |
Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning J. Foerster, N. Nardelli, G. Farquhar, T. Afouras, P. Torr, P. Kohli and S. Whiteson |
9h40 – 10h00 |
Constrained Bayesian Reinforcement Learning via Approximate Linear Programming - slides J. Lee, Y. Jang, P. Poupart and K. Kim |
10h00 – 11h40 |
Invited Talk:Scaling up RL with Offline Task Hierarchies Devin Schwab (Carnegie Mellon University) |
11h40 – 12h00 | Coffee break |
Session 2 | |
11h00 – 11h20 |
Automatic Object-Oriented Curriculum Generation for Reinforcement Learning F. L. Silva and A. H. R. Costa |
11h20 – 11h40 |
Count-Based Exploration in Feature Space for Reinforcement Learning J. Martin, S. Narayanan S., T. Everitt and M. Hutter |
11h40 – 12h00 |
Learning Multimodal Transition Dynamics for Model-Based Reinforcement Learning - slides T. M. Moerland, J. Broekens and C. M. Jonker |
12h00 – 12h40 |
Invited Talk:Scaling Up Policy Search Methods for Robotics Herke van Hoof (McGill University) |
12h40 - 12h45 | Closing / Community Meeting / Integration |