Guided policy search

30th International Conference on Machine Learning, ICML 2013

Volumn , Issue PART 2, 2013, Pages 1038-1046

  Levine, Sergey ^a   Koltun, Vladlen ^a  

^a Stanford University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

OPTIMIZATION;

DIFFERENTIAL DYNAMIC PROGRAMMING; DIRECT POLICY SEARCH; HIGH-DIMENSIONAL SYSTEMS; LEARNING NEURAL NETWORKS; POLICY LEARNING; POLICY OPTIMIZATION; POLICY SEARCH; TRAJECTORY OPTIMIZATION;

LEARNING SYSTEMS;

EID: 84897529781     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (23)

1. Abbeel, P., Coates, A., Quigley, M., and Ng, A. An application of reinforcement learning to aerobatic helicopter flight. In Advances in Neural Information Processing Systems (NIPS 19), 2006.
2. Atkeson, C. and Morimoto, J. Nonparametric representation of policies and value functions: A trajectory-based approach. In Advances in Neural Information Processing Systems (NIPS 15), 2002.
3. Atkeson, C. and Stephens, B. Random sampling of states in dynamic programming. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 38(4): 924-929, 2008.
4. Bagnell, A., Kakade, S., Ng, A., and Schneider, J. Policy search by dynamic programming. In Advances in Neural Information Processing Systems (NIPS 16), 2003.
5. Baxter, J., Bartlett, P., and Weaver, L. Experiments with infinite-horizon, policy-gradient estimation. Journal of Artificial Intelligence Research, 15: 351-381, 2001.
6. Deisenroth, M. and Rasmussen, C. PILCO: a model-based and data-efficient approach to policy search. In International Conference on Machine Learning (ICML), 2011.
7. Dvijotham, K. and Todorov, E. Inverse optimal control with linearly-solvable MDPs. In International Conference on Machine Learning (ICML), 2010.
8. Ijspeert, A., Nakanishi, J., and Schaal, S. Movement imitation with nonlinear dynamical systems in humanoid robots. In International Conference on Robotics and Automation, 2002.
9. Kakade, S. and Langford, J. Approximately optimal approximate reinforcement learning. In International Conference on Machine Learning (ICML), 2002.
10. Kalakrishnan, M., Chitta, S., Theodorou, E., Pastor, P., and Schaal, S. STOMP: stochastic trajectory optimization for motion planning. In International Conference on Robotics and Automation, 2011.
11. Kober, J. and Peters, J. Learning motor primitives for robotics. In International Conference on Robotics and Automation, 2009.
12. Peshkin, L. and Shelton, C. Learning from scarce experience. In International Conference on Machine Learning (ICML), 2002.
13. Peters, J. and Schaal, S. Reinforcement learning of motor skills with policy gradients. Neural Networks, 21(4):682-697, 2008.
14. Ross, S. and Bagnell, A. Agnostic system identification for model-based reinforcement learning. In International Conference on Machine Learning (ICML), 2012.
15. Ross, S., Gordon, G., and Bagnell, A. A reduction of imitation learning and structured prediction to no-regret online learning. Journal of Machine Learning Research, 15:627-635, 2011.
16. Sutton, R., McAllester, D., Singh, S., and Mansour, Y. Policy gradient methods for reinforcement learning with function approximation. In Advances in Neural Information Processing Systems (NIPS 11), 1999.
17. Tang, J. and Abbeel, P. On a connection between importance sampling and the likelihood ratio policy gradient. In Advances in Neural Information Processing Systems (NIPS 23), 2010.
18. Tassa, Y., Erez, T., and Todorov, E. Synthesis and stabilization of complex behaviors through online trajectory optimization. In IEEE/RSJ International Conference on Intelligent Robots and Systems, 2012.
19. Tedrake, R., Zhang, T., and Seung, H. Stochastic policy gradient reinforcement learning on a simple 3d biped. In IEEE/RSJ International Conference on Intelligent Robots and Systems, 2004.
20. Todorov, E., Erez, T., and Tassa, Y. MuJoCo: A physics engine for model-based control. In IEEE/RSJ International Conference on Intelligent Robots and Systems, 2012.
21. Whitman, E. and Atkeson, C. Control of a walking biped using a combination of simple policies. In 9th IEEE-RAS International Conference on Humanoid Robots, 2009.
22. Yin, K., Loken, K., and van de Panne, M. SIMBICON: simple biped locomotion control. ACM Transactions Graphics, 26(3), 2007.
23. Ziebart, B. Modeling purposeful adaptive behavior with the principle of maximum causal entropy. PhD thesis, Carnegie Mellon University, 2010.