Xueyu Wei, Lilong Duan, and Wei XueThis email address is being protected from spambots. You need JavaScript enabled to view it.

School of Computer Science and Technology, Anhui University of Technology, Maanshan 243032, China


Received: October 26, 2022
Accepted: March 8, 2023
Publication Date: May 2, 2023

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202312_26(12).0015  

In multi-goal reinforcement learning, an agent learns to achieve multiple goals using a goal-oriented policy, obtaining rewards from positions that have been achieved. Dynamic hindsight experience replay method improves the learning efficiency of the algorithm by matching the trajectories of past failed episodes and creating successful experiences. But these experiences are sampled and replayed by a random strategy, without considering the importance of the episode samples for learning. Therefore, not only bias is introduced as the training process, but also suboptimal improvements in terms of sample efficiency are obtained. To address these issues, this paper introduces a reward-weighted mechanism based on the dynamic hindsight experience replay (RDHER). We extend dynamic hindsight experience replay with a trade-off to make rewards calculated for hindsight experience numerically greater than actual rewards. Specifically, the hindsight rewards are multiplied by a weighting factor to increase the Q-value of the hindsight state–action pair, which drives the update of the policy to select the maximum action for the given hindsight transitions. Our experiments show that the hindsight bias can be reduced in training using the proposed method. Further, we demonstrate RDHER is effective in challenging robot manipulation tasks, and outperforms several other multi-goal baseline methods in terms of success rate.

Keywords: Reinforcement learning, Multi-goal learning, Hindsight experience replay, Hindsight bias, Reward-weighted

  1. [1]    V. François-Lavet, P. Henderson, R. Islam, M. G. Belle-mare, and J. Pineau, (2018) “An introduction to deep reinforcement learning" Foundations and Trends in Machine Learning 11(3-4): 219–354. DOI: 10.1561/ 2200000071.
  2. [2]    L. Busoniu, R. Babuska, B. De Schutter, and D. Ernst. Reinforcement learning and dynamic programming using function approximators. CRC press, 2017. 
  3. [3]    J. Schrittwieser, I. Antonoglou, T. Hubert, K. Si-monyan, L. Sifre, S. Schmitt, A. Guez, E. Lockhart, D. Hassabis, T. Graepel, T. Lillicrap, and D. Silver, (2020) “Mastering Atari, Go, chess and shogi by planning with a learned model" Nature 588(7839): 604–609. DOI: 10.1038/s41586-020-03051-4.
  4. [4]    D. Silver, J. Schrittwieser, K. Simonyan, I. Antonoglou, A. Huang, A. Guez, T. Hubert, L. Baker, M. Lai, A. Bolton, Y. Chen, T. Lillicrap, F. Hui, L. Sifre, G. Van Den Driessche, T. Graepel, and D. Hassabis, (2017) “Mastering the game of Go without human knowledge" Nature 550(7676): 354–359. DOI: 10.1038/nature24270.
  5. [5]    D. Silver, T. Hubert, J. Schrittwieser, I. Antonoglou, M. Lai, A. Guez, M. Lanctot, L. Sifre, D. Kumaran, T. Graepel, T. Lillicrap, K. Simonyan, and D. Hassabis, (2018) “A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play" Science 362(6419): 1140–1144. DOI: 10.1126/science.aar6404
  6. [6]    C. Berner, G. Brockman, B. Chan, V. Cheung, P. D˛ebiak, C. Dennison, D. Farhi, Q. Fischer, S. Hashme, C. Hesse, et al., (2019) “Dota 2 with large scale deep rein-forcement learning" arXiv preprint arXiv:1912.06680:
  7. [7] V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, and D. Hassabis, (2015) “Human-level control through deep reinforcement learning" Na-ture 518(7540): 529–533. DOI: 10.1038/nature14236
  8. [8] O. Vinyals, I. Babuschkin, W. M. Czarnecki, M. Math-ieu, A. Dudzik, J. Chung, D. H. Choi, R. Powell, T. Ewalds, P. Georgiev, J. Oh, D. Horgan, M. Kroiss, I. Danihelka, A. Huang, L. Sifre, T. Cai, J. P. Agapiou, M. Jaderberg, A. S. Vezhnevets, R. Leblond, T. Pohlen, V. Dalibard, D. Budden, Y. Sulsky, J. Molloy, T. L. Paine, C. Gulcehre, Z. Wang, T. Pfaff, Y. Wu, R. Ring, D. Yogatama, D. Wünsch, K. McKinney, O. Smith, T. Schaul, T. Lillicrap, K. Kavukcuoglu, D. Hassabis, C. Apps, and D. Silver, (2019) “Grandmaster level in StarCraft II using multi-agent reinforcement learning" Nature 575(7782): 350–354. DOI: 10.1038/s41586-019-1724-z
  9. [9] S. Levine, P. Pastor, A. Krizhevsky, J. Ibarz, and D. Quillen, (2018) “Learning hand-eye coordi-nation for robotic grasping with deep learning and large-scale data collection" International Journal of Robotics Research 37(4-5): 421–436. DOI: 10.1177/0278364917710318.
  10. [10] O. M. Andrychowicz, B. Baker, M. Chociej, R. Józe-fowicz, B. McGrew, J. Pachocki, A. Petron, M. Plap-pert, G. Powell, A. Ray, J. Schneider, S. Sidor, J. Tobin, P. Welinder, L. Weng, and W. Zaremba, (2020) “Learn-ing dexterous in-hand manipulation" International Journal of Robotics Research 39(1): 3–20. DOI: 10.1177/0278364919887447.
  11. [11] S. Gu, E. Holly, T. Lillicrap, and S. Levine. “Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates”. In: Cited by: 777; All Open Access, Green Open Access. 2017, 3389– 3396. DOI: 10.1109/ICRA.2017.7989385
  12. [12] M. Riedmiller, R. Hafner, T. Lampe, M. Neunert, J. Degrave, T. Van De Wiele, V. Mnih, N. Heess, and T. Springenberg. “Learning by playing - Solving sparse reward tasks from scratch”. In: 10. Cited by: 45. 2018, 6910–6919.
  13. [13] J. Xie, Z. Shao, Y. Li, Y. Guan, and J. Tan, (2019) “Deep Reinforcement Learning with Optimized Reward Func-tions for Robotic Trajectory Planning" IEEE Access 7: 105669–105679. DOI: 10.1109/ACCESS.2019.2932257.
  14. [14]    M. Plappert, M. Andrychowicz, A. Ray, B. McGrew, B. Baker, G. Powell, J. Schneider, J. Tobin, M. Chociej, P. Welinder, et al., (2018) “Multi-goal reinforcement learning: Challenging robotics environments and request for research" arXiv preprint arXiv:1802.09464:
  15. [15]   J. Tarbouriech, E. Garcelon, M. Valko, M. Pirotta, and A. Lazaric. “No-regret exploration in goal-oriented reinforcement learning”. In: PartF168147-13. Cited by: 5. 2020, 9370–9379.
  16. [16]    M. Andrychowicz, F. Wolski, A. Ray, J. Schneider, R. Fong, P. Welinder, B. McGrew, J. Tobin, P. Abbeel, and W. Zaremba. “Hindsight experience replay”. In: 2017-December. Cited by: 616. 2017, 5049–5059. 
  17. [17]    R. Zhao and V. Tresp. “Energy-based hindsight expe-rience prioritization”. In: Conference on Robot Learning. PMLR. 2018, 113–122.
  18. [18]    B. Manela and A. Biess, (2021) “Bias-reduced hindsight experience replay with virtual goal prioritization" Neu-rocomputing 451: 305–315. DOI: 10.1016/j.neucom. 2021.02.090.
  19. [19]   S. Lanka and T. Wu, (2018) “Archer: Aggressive rewards to counter bias in hindsight experience replay" arXiv preprint arXiv:1809.02070:
  20. [20]    C. Bai, L. Wang, Y. Wang, Z. Wang, R. Zhao, C. Bai, and P. Liu, (2023) “Addressing Hindsight Bias in Multi-goal Reinforcement Learning" IEEE Transactions on Cybernetics 53(1): 392–405. DOI: 10.1109/TCYB.2021. 3107202
  21. [21]    M. Fang, T. Zhou, Y. Du, L. Han, and Z. Zhang. “Curriculum-guided hindsight experience replay”.In: 32. Cited by: 43. 2019.
  22. [22]    M. L. Puterman. Markov decision processes: discrete stochastic dynamic programming. John Wiley & Sons, 2014. 
  23. [23]    Y. Wen, J. Si, A. Brandt, X. Gao, and H. H. Huang, (2020) “Online Reinforcement Learning Control for the Personalization of a Robotic Knee Prosthesis" IEEE Trans-actions on Cybernetics 50(6): 2346-2356.DOI: 10.1109/TCYB.2019.2890974.
  24. [24]    S. B. Niku. Introduction to robotics: analysis, control, applications. John Wiley & Sons, 2020.
  25. [25]   X. Wang, L. Ke, Z. Qiao, and X. Chai, (2021) “Large-Scale Traffic Signal Control Using a Novel Multiagent Reinforcement Learning" IEEE Transactions on Cy-bernetics 51(1): 174–187. DOI: 10.1109/TCYB.2020.3015811.
  26. [26] Y.-C. Liu and C.-Y. Huang, (2022) “DDPG-Based Adap-tive Robust Tracking Control for Aerial Manipulators With Decoupling Approach" IEEE Transactions on Cy-bernetics 52(8): 8258–8271. DOI: 10.1109/TCYB.2021. 3049555.
  27. [27]   M. Patacchiola and A. Cangelosi, (2022) “A Develop-mental Cognitive Architecture for Trust and Theory of Mind in Humanoid Robots" IEEE Transactions on Cy-bernetics 52(3): 1947–1959. DOI: 10.1109/TCYB.2020. 3002892.
  28. [28]   T. Schaul, D. Horgan, K. Gregor, and D. Silver. “Uni-versal value function approximators”. In: 2. Cited by: 364. 2015, 1312–1320.
  29. [29]   V. Pong, S. Gu, M. Dalal, and S. Levine, (2018) “Tem-poral difference models: Model-free deep rl for model-based control" arXiv preprint arXiv:1802.09081:
  30. [30]   S. Zhang and R. S. Sutton, (2017) “A deeper look at experience replay" arXiv preprint arXiv:1712.01275:
  31. [31] V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller, (2013) “Playing atari with deep reinforcement learning" arXiv preprint arXiv:1312.5602:
  32. [32] T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra, (2015) “Contin-uous control with deep reinforcement learning" arXiv preprint arXiv:1509.02971:
  33. [33]   M. Fang, C. Zhou, B. Shi, B. Gong, J. Xu, and T. Zhang. “Dher: Hindsight experience replay for dy-namic goals”. In: Cited by: 21. 2019.
  34. [34] L. F. Vecchietti, M. Seo, and D. Har, (2022) “Sampling Rate Decay in Hindsight Experience Replay for Robot Con-trol" IEEE Transactions on Cybernetics 52(3): 1515– 1526. DOI: 10.1109/TCYB.2020.2990722.
  35. [35] P. Rauber, A. Ummadisingu, F. Mutz, and J. Schmid-huber, (2017) “Hindsight policy gradients" arXiv preprint arXiv:1711.06006:
  36. [36]   A. Nair, V. Pong, M. Dalal, S. Bahl, S. Lin, and S. Levine. “Visual reinforcement learning with imag-ined goals”. In: 2018-December. Cited by: 137. 2018, 9191–9200.
  37. [37]   D. P. Kingma and M. Welling, (2013) “Auto-encoding variational bayes" arXiv preprint arXiv:1312.6114:
  38. [38] C. Bai, L. Wang, L. Han, J. Hao, A. Garg, P. Liu, and Z. Wang. “Principled exploration via optimistic boot-strapping and backward induction”. In: International Conference on Machine Learning. PMLR. 2021, 577–587.
  39. [39] H. Liu, A. Trott, R. Socher, and C. Xiong, (2019) “Competitive experience replay" arXiv preprint arXiv:1902.00528:
  40. [40] R. Zhao, X. Sun, and V. Tresp. “Maximum entropyregularized multi-goal reinforcement learning”. In: 2019-June. Cited by: 8. 2019, 13022–13035.
  41. [41] C. Colas, P. Founder, O. Sigaud, M. Chetouani, and P.-Y. Oudeyer. “CURIOUS: Intrinsically motivated modular multi-goal reinforcement learning”. In: 2019-June. Cited by: 9. 2019, 2372–2387.
  42. [42] Y. Ding, C. Florensa, M. Phielipp, and P. Abbeel. “Goal-conditioned imitation learning”. In: 32. Cited by: 43. 2019.
  43. [43] J. Yang, A. Nakhaei, D. Isele, K. Fujimura, and H. Zha, (2018) “Cm3: Cooperative multi-goal multi-stage multi-agent reinforcement learning" arXiv preprint arXiv:1809.05188:
  44. [44] S. Nasiriany, V. H. Pong, S. Lin, and S. Levine. “Planning with goal-conditioned policies”. In: 32. Cited by: 38. 2019.
  45. [45] E. Todorov, T. Erez, and Y. Tassa. “MuJoCo: A physics engine for model-based control”. In: Cited by: 1643. 2012, 5026–5033. DOI: 10.1109/IROS.2012.6386109.


42nd percentile
Powered by  Scopus

SCImago Journal & Country Rank

Enter your name and email below to receive latest published articles in Journal of Applied Science and Engineering.