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2019 Volume 34
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ORIGINAL RESEARCH   Open Access    

Introspective Q-learning and learning from demonstration

  • Deramore Ln, Heslington, York YO10 5GH

  • Artificial Intelligence Lab Pleinlaan 9, 3th floor 1050, Brussels

  • St Petersburg

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  • Abstract: One challenge faced by reinforcement learning (RL) agents is that in many environments the reward signal is sparse, leading to slow improvement of the agent’s performance in early learning episodes. Potential-based reward shaping can help to resolve the aforementioned issue of sparse reward by incorporating an expert’s domain knowledge into the learning through a potential function. Past work on reinforcement learning from demonstration (RLfD) directly mapped (sub-optimal) human expert demonstration to a potential function, which can speed up RL. In this paper we propose an introspective RL agent that significantly further speeds up the learning. An introspective RL agent records its state–action decisions and experience during learning in a priority queue. Good quality decisions, according to a Monte Carlo estimation, will be kept in the queue, while poorer decisions will be rejected. The queue is then used as demonstration to speed up RL via reward shaping. A human expert’s demonstration can be used to initialize the priority queue before the learning process starts. Experimental validation in the 4-dimensional CartPole domain and the 27-dimensional Super Mario AI domain shows that our approach significantly outperforms non-introspective RL and state-of-the-art approaches in RLfD in both domains.
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  • Argall , B. D., Chernova , S., Veloso , M. & Browning , B. 2009. A survey of robot learning from demonstration. Robotics and Autonomous Systems 57(5), 469–483.

    Google Scholar

    Bellemare , M., Srinivasan , S., Ostrovski , G., Schaul , T., Saxton , D., & Munos , R. (2016). Unifying count-based exploration and intrinsic motivation. In Proceedings of the 30th Conference on Advances in Neural Information Processing Systems (pp. 1471–1479).

    Google Scholar

    Brys , T. 2016. Reinforcement Learning with Heuristic Information. PhD thesis, Vrije Universiteit Brussel.

    Google Scholar

    Brys , T., Harutyunyan , A., Suay , H. B., Chernova , S., Taylor , M. E. & Nowé , A. 2015. Reinforcement learning from demonstration through shaping. In IJCAI. 3352–3358.

    Google Scholar

    Devlin , S. & Kudenko , D. 2012. Dynamic potential-based reward shaping. In Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems, Vol. 1, 433–440. International Foundation for Autonomous Agents and Multiagent Systems.

    Google Scholar

    Harutyunyan , A., Devlin , S., Vrancx , P. & Nowé , A. 2015. Expressing arbitrary reward functions as potential-based advice. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence.

    Google Scholar

    Karakovskiy , S. & Togelius , J. 2012. The Mario AI benchmark and competitions. IEEE Transactions on Computational Intelligence and AI in Games 4, 55–67.

    Google Scholar

    Mataric , M. J. 1994. Reward functions for accelerated learning. In Machine Learning: Proceedings of the Eleventh International Conference, 181–189.

    Google Scholar

    Michie , D. & Chambers , R. A. 1968. Boxes: an experiment in adaptive control. Machine Intelligence 2, 137–152.

    Google Scholar

    Ng , A. Y., Harada , D. & Russell , S. 1999. Policy invariance under reward transformations: theory and application to reward shaping. In Proceedings of the Sixteenth International Conference on Machine Learning. Vol. 99, 278–287.

    Google Scholar

    Ng , A. Y., Andrew , Y., & Russell , S. J. 2000. Algorithms for inverse reinforcement learning. In ICML 1, 663–670.

    Google Scholar

    Pathak , D., Agrawal , P., Efros , A. A. & Darrell , T. 2017. Curiosity-driven exploration by self-supervised prediction. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. (pp. 16–17).

    Google Scholar

    Schaal , S. 1997. Learning from demonstration. Advances in Neural Information Processing Systems 9, 1040–1046.

    Google Scholar

    Schaul , T., Quan , J., Antonoglou , I. & Silver , D. 2015. Prioritized experience replay. arXiv preprint arXiv:1511.05952.

    Google Scholar

    Silver , D., Huang , A., Maddison , C. J., Guez , A., Sifre , L., Van Den Driessche , G., Schrittwieser , J., Antonoglou , I., Panneershelvam , V., Lanctot , M. & et al. 2016. Mastering the game of go with deep neural networks and tree search. Nature 529, 484–489.

    Google Scholar

    Singh , S. P. & Sutton , R. S. 1996. Reinforcement learning with replacing eligibility traces. Machine Learning 22, 123–158.

    Google Scholar

    Smart , W. D. & Kaelbling , L. P. 2002. Effective reinforcement learning for mobile robots. In IEEE International Conference on Robotics and Automation, Vol. 4, 3404–3410. IEEE.

    Google Scholar

    Suay , H. B., Brys , T., Taylor , M. E. & Chernova , S. 2016. Learning from demonstration for shaping through inverse reinforcement learning. In Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems, 429–437. International Foundation for Autonomous Agents and Multiagent Systems.

    Google Scholar

    Sutton , R. & Barto , A. 1998. Reinforcement Learning: An Introduction, Vol. 1. Cambridge University Press.

    Google Scholar

    Taylor , M. E. & Stone , P. 2009. Transfer learning for reinforcement learning domains: a survey. Journal of Machine Learning Research 10, 1633–1685.

    Google Scholar

    Taylor , M. E., Suay , H. B. & Chernova , S. 2011. Integrating reinforcement learning with human demonstrations of varying ability. In The 10th International Conference on Autonomous Agents and Multiagent Systems, Vol. 2, 617–624. International Foundation for Autonomous Agents and Multiagent Systems.

    Google Scholar

    Tsitsiklis , J. N. 1994. Asynchronous stochastic approximation and Q-learning. Machine Learning 16, 185–202.

    Google Scholar

    Watkins , C. J. C. H. 1989. Learning from Delayed Rewards. PhD thesis, University of Cambridge.

    Google Scholar

    Wiewiora , E., Cottrell , G. & Elkan , C. 2003. Principled methods for advising reinforcement learning agents. In ICML. 792–799.

    Google Scholar

  • Cite this article

    Mao Li, Tim Brys, Daniel Kudenko. 2019. Introspective Q-learning and learning from demonstration. The Knowledge Engineering Review. 34:31 doi: 10.1017/S0269888919000031
    Mao Li, Tim Brys, Daniel Kudenko. 2019. Introspective Q-learning and learning from demonstration. The Knowledge Engineering Review. 34:31 doi: 10.1017/S0269888919000031
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ORIGINAL RESEARCH   Open Access    

Introspective Q-learning and learning from demonstration

  • Deramore Ln, Heslington, York YO10 5GH

  • Artificial Intelligence Lab Pleinlaan 9, 3th floor 1050, Brussels

  • St Petersburg

  • Received: 13 November 2018
  • Revised: 06 March 2019
  • Accepted: 31 May 2019
The Knowledge Engineering Review  34,  Article number: e8  (2019)  |  Cite this article

Abstract: Abstract: One challenge faced by reinforcement learning (RL) agents is that in many environments the reward signal is sparse, leading to slow improvement of the agent’s performance in early learning episodes. Potential-based reward shaping can help to resolve the aforementioned issue of sparse reward by incorporating an expert’s domain knowledge into the learning through a potential function. Past work on reinforcement learning from demonstration (RLfD) directly mapped (sub-optimal) human expert demonstration to a potential function, which can speed up RL. In this paper we propose an introspective RL agent that significantly further speeds up the learning. An introspective RL agent records its state–action decisions and experience during learning in a priority queue. Good quality decisions, according to a Monte Carlo estimation, will be kept in the queue, while poorer decisions will be rejected. The queue is then used as demonstration to speed up RL via reward shaping. A human expert’s demonstration can be used to initialize the priority queue before the learning process starts. Experimental validation in the 4-dimensional CartPole domain and the 27-dimensional Super Mario AI domain shows that our approach significantly outperforms non-introspective RL and state-of-the-art approaches in RLfD in both domains.

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    • As opposed to the external expert demonstrations used in their work, we encode the agent’s own filtered experiences in the potential function.

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    Cite this article
    Mao Li, Tim Brys, Daniel Kudenko. 2019. Introspective Q-learning and learning from demonstration. The Knowledge Engineering Review. 34:31 doi: 10.1017/S0269888919000031
    Mao Li, Tim Brys, Daniel Kudenko. 2019. Introspective Q-learning and learning from demonstration. The Knowledge Engineering Review. 34:31 doi: 10.1017/S0269888919000031
    shu

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