Reinforcement Learning for Humanoid Robotics

  title={Reinforcement Learning for Humanoid Robotics},
  author={Jan Peters and Sethu Vijayakumar and Stefan Schaal},
Reinforcement learning offers one of the most general framework to take traditional robotics towards true autonomy and versatility. However, applying reinforcement learning to high dimensional movement systems like humanoid robots remains an unsolved problem. In this paper, we discuss different approaches of reinforcement learning in terms of their applicability in humanoid robotics. Methods can be coarsely classified into three different categories, i.e., greedy methods, ‘vanilla’ policy… CONTINUE READING
Highly Influential
This paper has highly influenced 18 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 317 citations. REVIEW CITATIONS
183 Citations
29 References
Similar Papers


Publications citing this paper.
Showing 1-10 of 183 extracted citations

318 Citations

Citations per Year
Semantic Scholar estimates that this publication has 318 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.
Showing 1-10 of 29 references

Reinforcement Learning, The MIT Press

  • R. S. Sutton, A. G. Barto
  • 1998
Highly Influential
6 Excerpts

Reinforcement Learning for Continuous Action using Stochastic Gradient Ascent

  • H. Kimura, S. Kobayashi
  • The 5th International Conference on Intelligent…
  • 1998
Highly Influential
5 Excerpts

Similar Papers

Loading similar papers…