amoliu / sf-deep-rl

Project on Successor Features in Deep Reinforcement Learning and Transfer Learning

Successor Features in Deep Reinforcement Learning and Transfer Learning

This project takes part into the master 'MVA'. It is based on the following papers :

• Barreto, A., Dabney, W., Munos, R., Hunt, J. J., Schaul, T., Silver, D., & van Hasselt, H. P. (2017). Successor features for transfer in reinforcement learning. In Advances in Neural Information Processing Systems (pp. 4058-4068).

• Kulkarni, T. D., Saeedi, A., Gautam, S., & Gershman, S. J. (2016). Deep successor reinforcement learning. arXiv preprint arXiv:1606.02396.

• Lehnert, Lucas, Stefanie Tellex, and Michael L.Littman. Advantages and Limitations of using Successor Features for Transfer in Reinforcement Learning. arXiv preprint arXiv:1708.00102 (2017).

Instructions

• deadline : unknown for now (around 17/01/2018)
• final report will have 5-10 pages
• formatting of submission: NIPS forma
• time your presentation for 15 minutes (strict!)