pith. machine review for the scientific record. sign in

arxiv: 2604.03023 · v1 · submitted 2026-04-03 · 💻 cs.RO

Recognition: 2 theorem links

· Lean Theorem

Behavior-Constrained Reinforcement Learning with Receding-Horizon Credit Assignment for High-Performance Control

Siwei Ju , Jan Tauberschmidt , Oleg Arenz , Peter van Vliet , Jan Peters

Authors on Pith no claims yet

Pith reviewed 2026-05-13 19:26 UTC · model grok-4.3

classification 💻 cs.RO
keywords behavior-constrained reinforcement learningreceding-horizon credit assignmentexpert trajectory alignmenthigh-performance controlrace car simulationimitation qualityreference trajectory conditioning
0
0 comments X

The pith

A receding-horizon credit assignment mechanism lets reinforcement learning improve past expert demonstrations while keeping trajectory-level behavior consistent in high-dynamic control.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces a behavior-constrained RL framework that adds explicit control over deviation from expert trajectories in tasks where performance and style must both be preserved. It replaces standard reward signals with look-ahead rewards drawn from short-term future trajectory predictions, so credit assignment accounts for behavior consistency over horizons rather than single steps. The policy is further conditioned on reference trajectories to capture the natural spread of expert responses under changing conditions. In high-fidelity race-car simulation with professional driver data, the resulting policies reach competitive lap times and reproduce expert driving characteristics more closely than standard RL or imitation baselines. Human-in-the-loop evaluation confirms that the learned controllers exhibit setup-dependent traits that align with professional driver feedback.

Core claim

The central claim is that receding-horizon predictive modeling supplies look-ahead rewards that enforce trajectory-level consistency with expert behavior during RL training, while reference-trajectory conditioning allows the policy to represent a distribution of expert-consistent actions; this combination yields controllers that surpass demonstration performance yet remain aligned with human driving style in extreme-dynamics domains such as professional race-car control.

What carries the argument

The receding-horizon predictive mechanism that generates short-term future trajectories and converts them into look-ahead rewards for behavior consistency, paired with conditioning the policy on reference trajectories.

If this is right

  • Policies can exceed expert data performance while preserving measurable alignment on full trajectories rather than single actions.
  • The same framework applies to any dynamic control task where both optimality and human-like behavior matter, such as vehicle or robot control under disturbances.
  • Conditioning on reference trajectories lets one policy represent the variability seen in expert demonstrations instead of collapsing to a single deterministic target.
  • Human-grounded evaluations become feasible because the learned behavior already matches setup-dependent characteristics reported by professionals.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The approach could be tested on other high-speed or safety-critical domains such as autonomous highway driving where both speed and predictable style affect human acceptance.
  • If the receding-horizon horizon length can be learned rather than fixed, the method might generalize across tasks with different time scales without additional hyperparameter search.
  • Deploying these policies as human surrogates in simulation-to-real transfer would be more reliable than pure RL because behavioral deviation is explicitly bounded during training.

Load-bearing premise

The receding-horizon mechanism reliably supplies effective look-ahead rewards that maintain expert behavior consistency without causing training instability or demanding extensive domain-specific tuning.

What would settle it

Training identical policies without the receding-horizon component and observing either a sharp drop in lap-time performance or a large increase in trajectory deviation from expert data would show the mechanism is not delivering the claimed joint improvement.

Figures

Figures reproduced from arXiv: 2604.03023 by Jan Peters, Jan Tauberschmidt, Oleg Arenz, Peter van Vliet, Siwei Ju.

Figure 1
Figure 1. Figure 1: Application workflow of the proposed method in professional motor [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the policy structure with the components and their [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: A schematic diagram for reward terms. a 5-second horizon with 0.25 seconds in between and converted into the car’s local coordinates as well. • Current actions. As we are using relative actions and the agent needs to know the current position of the pedals and steering wheel, they are fed into the policy as well. Policy π: The network structure is shown in [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Throughout the early training stages, all experimental [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: The Pareto fronts for different methods. Since both lap time and Mean [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 4
Figure 4. Figure 4: Learning curves of Lap Time and Mean Offset of Experiment B, C, [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 6
Figure 6. Figure 6: Single lap compared to the reference trajectory. [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Driving lines of agent laps and their corresponding reference [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Learning progress of experiment S-I, S-II und S-II, using reward [PITH_FULL_IMAGE:figures/full_fig_p009_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Lap time distributions across three vehicle setups. Each curve shows [PITH_FULL_IMAGE:figures/full_fig_p010_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Time-series comparison of control and state variables for 10 laps [PITH_FULL_IMAGE:figures/full_fig_p011_10.png] view at source ↗
Figure 9
Figure 9. Figure 9: Consistent with the race engineers’ prior knowledge, [PITH_FULL_IMAGE:figures/full_fig_p012_9.png] view at source ↗
Figure 11
Figure 11. Figure 11: Comparison of four different race car setups by ranking the driven [PITH_FULL_IMAGE:figures/full_fig_p012_11.png] view at source ↗
read the original abstract

Learning high-performance control policies that remain consistent with expert behavior is a fundamental challenge in robotics. Reinforcement learning can discover high-performing strategies but often departs from desirable human behavior, whereas imitation learning is limited by demonstration quality and struggles to improve beyond expert data. We propose a behavior-constrained reinforcement learning framework that improves beyond demonstrations while explicitly controlling deviation from expert behavior. Because expert-consistent behavior in dynamic control is inherently trajectory-level, we introduce a receding-horizon predictive mechanism that models short-term future trajectories and provides look-ahead rewards during training. To account for the natural variability of human behavior under disturbances and changing conditions, we further condition the policy on reference trajectories, allowing it to represent a distribution of expert-consistent behaviors rather than a single deterministic target. Empirically, we evaluate the approach in high-fidelity race car simulation using data from professional drivers, a domain characterized by extreme dynamics and narrow performance margins. The learned policies achieve competitive lap times while maintaining close alignment with expert driving behavior, outperforming baseline methods in both performance and imitation quality. Beyond standard benchmarks, we conduct human-grounded evaluation in a driver-in-the-loop simulator and show that the learned policies reproduce setup-dependent driving characteristics consistent with the feedback of top-class professional race drivers. These results demonstrate that our method enables learning high-performance control policies that are both optimal and behavior-consistent, and can serve as reliable surrogates for human decision-making in complex control systems.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes a behavior-constrained reinforcement learning framework that augments standard RL with a receding-horizon predictive mechanism to supply trajectory-level look-ahead rewards and conditions the policy on reference trajectories to capture variability in expert behavior. The central claim is that this enables policies that exceed demonstration performance while remaining aligned with expert driving, evaluated in high-fidelity race-car simulation with professional driver data and via human-grounded driver-in-the-loop tests.

Significance. If the empirical claims hold after addressing validation gaps, the work would offer a practical bridge between imitation and reinforcement learning for high-performance robotics, particularly in domains with narrow margins and extreme dynamics. The human-grounded evaluation and use of real professional driver data are strengths that could position the method as a reliable surrogate for human decision-making in control systems.

major comments (2)
  1. [§3.2] §3.2 (Receding-horizon predictive mechanism): The predictor is presented as supplying stable look-ahead rewards for behavior consistency, yet the manuscript provides no training procedure, accuracy metrics under extreme vehicle dynamics, or robustness checks against model mismatch and disturbances; this assumption is load-bearing for the credit-assignment claim and must be substantiated with targeted experiments.
  2. [§5] §5 (Empirical evaluation): The reported outperformance in lap times and imitation quality lacks quantitative metrics, explicit baseline definitions, statistical significance tests, and ablations on the behavior-constraint weight and horizon length; without these, the central performance and alignment claims remain only moderately supported despite the high-fidelity simulation setup.
minor comments (2)
  1. [§3] Notation for the conditioned policy and reference-trajectory input is introduced without a clear diagram or pseudocode; adding one would improve readability of the overall architecture.
  2. [Abstract] The abstract states that policies 'outperform baseline methods' but does not name the baselines; this detail should appear in the abstract for immediate clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We are grateful to the referee for the thorough review and constructive suggestions. We have revised the manuscript to address the major comments point by point as detailed below.

read point-by-point responses

  1. Referee: [§3.2] §3.2 (Receding-horizon predictive mechanism): The predictor is presented as supplying stable look-ahead rewards for behavior consistency, yet the manuscript provides no training procedure, accuracy metrics under extreme vehicle dynamics, or robustness checks against model mismatch and disturbances; this assumption is load-bearing for the credit-assignment claim and must be substantiated with targeted experiments.

    Authors: We concur that the receding-horizon predictive mechanism requires more detailed validation to support the credit assignment approach. In the revised manuscript, we have included the complete training procedure for the predictor, along with quantitative accuracy metrics assessed in extreme vehicle dynamics scenarios from our race car simulations. Additionally, we present robustness analyses under model mismatch and disturbances, which confirm the stability of the look-ahead rewards. These additions directly address the load-bearing assumption. revision: yes

  2. Referee: [§5] §5 (Empirical evaluation): The reported outperformance in lap times and imitation quality lacks quantitative metrics, explicit baseline definitions, statistical significance tests, and ablations on the behavior-constraint weight and horizon length; without these, the central performance and alignment claims remain only moderately supported despite the high-fidelity simulation setup.

    Authors: We agree that the empirical section would benefit from enhanced quantitative rigor. The updated manuscript now provides explicit definitions and implementations of the baseline methods, additional quantitative metrics beyond lap times (including trajectory deviation measures), results of statistical significance testing, and ablation studies varying the behavior-constraint weight and the horizon length. These revisions offer more robust evidence for the performance and alignment claims. revision: yes

Circularity Check

0 steps flagged

No circularity in derivation chain; empirical framework self-contained

full rationale

The paper proposes a behavior-constrained RL method with a receding-horizon predictive mechanism for trajectory-level credit assignment and policy conditioning on reference trajectories. No equations, fitted parameters renamed as predictions, or self-citation chains are present in the provided text that would reduce the central claims (competitive lap times with behavior alignment) to self-definitional inputs or prior author results by construction. Validation relies on external high-fidelity simulation benchmarks and human-grounded driver-in-the-loop evaluation, independent of internal definitions. This is a standard empirical contribution with no load-bearing circular steps.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The framework rests on standard RL assumptions plus the domain-specific premise that expert behavior variability can be captured by conditioning on reference trajectories; no new invented entities are introduced, but the receding-horizon component functions as an added modeling choice.

free parameters (1)
  • behavior constraint weight and horizon length
    Hyperparameters balancing deviation penalty against performance reward and determining look-ahead steps; these must be chosen or tuned for the specific dynamics.
axioms (1)
  • domain assumption Expert behavior under disturbances is adequately represented by a conditional distribution over reference trajectories
    Invoked to justify policy conditioning as a way to model natural human variability.

pith-pipeline@v0.9.0 · 5568 in / 1225 out tokens · 35646 ms · 2026-05-13T19:26:38.498296+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

50 extracted references · 50 canonical work pages · 3 internal anchors

  1. [1]

    Generating Personas for Games with Multimodal Adversarial Imitation Learn- ing, August 2023

    William Ahlberg, Alessandro Sestini, Konrad Toll- mar, and Linus Gisslén. Generating Personas for Games with Multimodal Adversarial Imitation Learn- ing, August 2023. URL http://arxiv.org/abs/2308.07598. arXiv:2308.07598 [cs]. TABLE X ADDITIONAL EXPERIMENT RESULT FOREXPB αs lap time MRO 1 66.443±0.029 0.4159±0.0468 0.8 66.673±0.020 0.4108±0.0108 0.6 67.53...

    work page arXiv 2023

  2. [2]

    Review and Comparison of Path Tracking Based on Model Predictive Control.Elec- tronics, 8(10):1077, October 2019

    Guoxing Bai, Yu Meng, Li Liu, Weidong Luo, Qing Gu, and Li Liu. Review and Comparison of Path Tracking Based on Model Predictive Control.Elec- tronics, 8(10):1077, October 2019. ISSN 2079-9292. doi: 10.3390/electronics8101077. URL https://www. mdpi.com/2079-9292/8/10/1077. Number: 10 Publisher: Multidisciplinary Digital Publishing Institute

    work page doi:10.3390/electronics8101077 2019

  3. [3]

    A framework for behavioural cloning

    Michael Bain and Claude Sammut. A framework for behavioural cloning. InMachine Intelligence 15,

    work page

  4. [4]

    URL https://api.semanticscholar.org/CorpusID: 10738655

    work page

  5. [5]

    Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing.IEEE Open Journal of Intelligent Transportation Systems, 3: 458–488, 2022

    Johannes Betz, Hongrui Zheng, Alexander Liniger, Ugo Rosolia, Phillip Karle, Madhur Behl, Venkat Krovi, and Rahul Mangharam. Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing.IEEE Open Journal of Intelligent Transportation Systems, 3: 458–488, 2022. ISSN 2687-7813. doi: 10.1109/ojits. 2022.3181510. URL http://arxiv.org/abs/2202.07008....

    work page doi:10.1109/ojits 2022

  6. [6]

    Modeling Human Driving Behavior through Generative Adversarial Imitation Learning.IEEE Transactions on Intelligent Transportation Systems, 24 (3):2874–2887, March 2023

    Raunak Bhattacharyya, Blake Wulfe, Derek Phillips, Alex Kuefler, Jeremy Morton, Ransalu Senanayake, and Mykel Kochenderfer. Modeling Human Driving Behavior through Generative Adversarial Imitation Learning.IEEE Transactions on Intelligent Transportation Systems, 24 (3):2874–2887, March 2023. ISSN 1524-9050, 1558-

    work page 2023

  7. [7]

    URL http: //arxiv.org/abs/2006.06412

    doi: 10.1109/TITS.2022.3227738. URL http: //arxiv.org/abs/2006.06412. arXiv:2006.06412 [cs]

    work page doi:10.1109/tits.2022.3227738 2022

  8. [8]

    Comparison of direct and indirect methods for minimum lap time optimal control problems

    Nicola Dal Bianco, Enrico Bertolazzi, Francesco Biral, and Matteo Massaro. Comparison of direct and indirect methods for minimum lap time optimal control problems. Vehicle System Dynamics, 57(5):665–696, 2019. doi: 10. 1080/00423114.2018.1480048. URL https://doi.org/10. 1080/00423114.2018.1480048

    work page arXiv 2019

  9. [9]

    A Study of Reinforcement Learning Techniques for Path Tracking in Autonomous Vehicles

    Jason Chemin, Ashley Hill, Eric Lucet, and Aurélien Mayoue. A Study of Reinforcement Learning Techniques for Path Tracking in Autonomous Vehicles. In2024 IEEE Intelligent Vehicles Symposium (IV), pages 1442–1449, June 2024. doi: 10.1109/IV55156.2024.10588521. URL https://ieeexplore.ieee.org/document/10588521. ISSN: 2642-7214

    work page doi:10.1109/iv55156.2024.10588521 2024

  10. [10]

    Implementation of the Pure Pursuit Path Tracking Algorithm

    Craig Coulter. Implementation of the Pure Pursuit Path Tracking Algorithm. 1992. URL https://www.semanticscholar.org/paper/ Implementation-of-the-Pure-Pursuit-Path-Tracking-Coulter/ ee756e53b6a68cb2e7a2e5d537a3eff43d793d70

    work page 1992

  11. [11]

    A Divergence Minimization Perspective on Imitation Learning Methods

    Seyed Kamyar Seyed Ghasemipour, Richard Zemel, and Shixiang Gu. A Divergence Minimization Perspective on Imitation Learning Methods. November 2019. doi: 10. 48550/arXiv.1911.02256. URL http://arxiv.org/abs/1911. 02256. arXiv:1911.02256 [cs]

    work page arXiv 2019

  12. [12]

    World Models

    David Ha and Jürgen Schmidhuber. World Models. March 2018. doi: 10.5281/zenodo.1207631. URL http: //arxiv.org/abs/1803.10122. arXiv:1803.10122 [cs]

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.5281/zenodo.1207631 2018

  13. [13]

    Dream to Control: Learning Behaviors by Latent Imagination

    Danijar Hafner, Timothy Lillicrap, Jimmy Ba, and Mo- hammad Norouzi. Dream to Control: Learning Behaviors by Latent Imagination, March 2020. URL http://arxiv. org/abs/1912.01603. arXiv:1912.01603 [cs]

    work page internal anchor Pith review Pith/arXiv arXiv 2020

  14. [14]

    Con- textual Markov Decision Processes

    Assaf Hallak, Dotan Di Castro, and Shie Mannor. Con- textual Markov Decision Processes. February 2015. doi: 10.48550/arXiv.1502.02259. URL http://arxiv.org/abs/ 1502.02259. arXiv:1502.02259 [stat]

    work page doi:10.48550/arxiv.1502.02259 2015

  15. [15]

    Hayes, Roxana R ˘adulescu, Eugenio Bargiacchi, Johan Källström, Matthew Macfarlane, Mathieu Rey- mond, Timothy Verstraeten, Luisa M

    Conor F. Hayes, Roxana R ˘adulescu, Eugenio Bargiacchi, Johan Källström, Matthew Macfarlane, Mathieu Rey- mond, Timothy Verstraeten, Luisa M. Zintgraf, Richard Dazeley, Fredrik Heintz, Enda Howley, Athirai A. Iris- sappane, Patrick Mannion, Ann Nowé, Gabriel Ramos, Marcello Restelli, Peter Vamplew, and Diederik M. Roi- jers. A Practical Guide to Multi-Obj...

    work page doi:10.1007/s10458-022-09552-y 2022

  16. [16]

    Generative adversarial imitation learning,

    Jonathan Ho and Stefano Ermon. Generative Adversarial Imitation Learning, June 2016. URL http://arxiv.org/abs/ 1606.03476. arXiv:1606.03476 [cs]

    work page arXiv 2016

  17. [17]

    Autonomous Automobile Trajec- tory Tracking for Off-Road Driving: Controller Design, Experimental Validation and Racing

    Gabriel Hoffmann, Claire Tomlin, Michael Montemerlo, and Sebastian Thrun. Autonomous Automobile Trajec- tory Tracking for Off-Road Driving: Controller Design, Experimental Validation and Racing. pages 2296–2301, August 2007. doi: 10.1109/ACC.2007.4282788

    work page doi:10.1109/acc.2007.4282788 2007

  18. [18]

    Intro- ducing Probabilistic Bézier Curves for N-Step Sequence Prediction.Proceedings of the AAAI Conference on Artificial Intelligence, 34(06):10162–10169, April 2020

    Ronny Hug, Wolfgang Hübner, and Michael Arens. Intro- ducing Probabilistic Bézier Curves for N-Step Sequence Prediction.Proceedings of the AAAI Conference on Artificial Intelligence, 34(06):10162–10169, April 2020. ISSN 2374-3468, 2159-5399. doi: 10.1609/aaai.v34i06

    work page doi:10.1609/aaai.v34i06 2020

  19. [19]

    URL https://ojs.aaai.org/index.php/AAAI/article/ view/6576

    work page

  20. [20]

    Digital Twin of a Driver-in-the-Loop Race Car Simulation With Contextual Reinforcement Learning

    Siwei Ju, Peter Van Vliet, Oleg Arenz, and Jan Pe- ters. Digital Twin of a Driver-in-the-Loop Race Car Simulation With Contextual Reinforcement Learning. IEEE Robotics and Automation Letters, 8(7):4107–4114, July 2023. ISSN 2377-3766, 2377-3774. doi: 10. 1109/LRA.2023.3279618. URL https://ieeexplore.ieee. org/document/10132573/

    work page arXiv 2023

  21. [21]

    Style-biased reinforcement learning for quadruped loco- motion.Under review at ICRA, 2025

    Siwei Ju, Oleg Arenz, Peter van Vliet, and Jan Peters. Style-biased reinforcement learning for quadruped loco- motion.Under review at ICRA, 2025

    work page 2025

  22. [22]

    Zeilinger

    Juraj Kabzan, Lukas Hewing, Alexander Liniger, and Melanie N. Zeilinger. Learning-Based Model Predic- tive Control for Autonomous Racing.IEEE Robotics and Automation Letters, 4(4):3363–3370, October 2019. ISSN 2377-3766, 2377-3774. doi: 10.1109/LRA.2019. 2926677. URL https://ieeexplore.ieee.org/document/ 8754713/

    work page doi:10.1109/lra.2019 2019

  23. [23]

    Imitation Learning as $f$-Divergence Minimization, May 2020

    Liyiming Ke, Sanjiban Choudhury, Matt Barnes, Wen Sun, Gilwoo Lee, and Siddhartha Srinivasa. Imitation Learning as $f$-Divergence Minimization, May 2020. arXiv:1905.12888

    work page arXiv 2020

  24. [24]

    Identification and modelling of race driving styles.Vehicle System Dynamics, 60(8):2890–2918, Au- gust 2022

    Stefan Löckel, André Kretschi, Peter Van Vliet, and Jan Peters. Identification and modelling of race driving styles.Vehicle System Dynamics, 60(8):2890–2918, Au- gust 2022. ISSN 0042-3114, 1744-5159. doi: 10.1080/ 00423114.2021.1930070. URL https://www.tandfonline. com/doi/full/10.1080/00423114.2021.1930070

    work page doi:10.1080/00423114.2021.1930070 2022

  25. [25]

    An Adaptive Human Driver 14 Model for Realistic Race Car Simulations.IEEE Trans- actions on Systems, Man, and Cybernetics: Systems, 53(11):6718–6730, November 2023

    Stefan Löckel, Siwei Ju, Maximilian Schaller, Peter Van Vliet, and Jan Peters. An Adaptive Human Driver 14 Model for Realistic Race Car Simulations.IEEE Trans- actions on Systems, Man, and Cybernetics: Systems, 53(11):6718–6730, November 2023. ISSN 2168-2216, 2168-2232. doi: 10.1109/TSMC.2023.3285588. URL https://ieeexplore.ieee.org/document/10173774/

    work page doi:10.1109/tsmc.2023.3285588 2023

  26. [26]

    Milliken and Douglas L

    William F. Milliken and Douglas L. Milliken.Race Car Vehicle Dynamics. Society of Automotive Engineers Warrendale, 1995

    work page 1995

  27. [27]

    Dynamic pref- erences in multi-criteria reinforcement learning

    Sriraam Natarajan and Prasad Tadepalli. Dynamic pref- erences in multi-criteria reinforcement learning. In Proceedings of the 22nd international conference on Machine learning, ICML ’05, pages 601–608, New York, NY , USA, August 2005. Association for Computing Machinery. ISBN 978-1-59593-180-1. doi: 10.1145/ 1102351.1102427. URL https://dl.acm.org/doi/10....

    work page arXiv 2005

  28. [28]

    Bridging State and History Represen- tations: Understanding Self-Predictive RL, April 2024

    Tianwei Ni, Benjamin Eysenbach, Erfan Seyedsalehi, Michel Ma, Clement Gehring, Aditya Mahajan, and Pierre-Luc Bacon. Bridging State and History Represen- tations: Understanding Self-Predictive RL, April 2024. URL http://arxiv.org/abs/2401.08898. arXiv:2401.08898 [cs]

    work page arXiv 2024

  29. [29]

    Model-free probabilistic movement primitives for physical interaction

    Alexandros Paraschos, Elmar Rueckert, Jan Peters, and Gerhard Neumann. Model-free probabilistic movement primitives for physical interaction. InIEEE/RSJ Inter- national Conference on Intelligent Robots and Systems (IROS), pages 2860–2866. IEEE, 2015

    work page 2015

  30. [30]

    Deepmimic: Example-guided deep re- inforcement learning of physics-based character skills

    Xue Bin Peng, Pieter Abbeel, Sergey Levine, and Michiel van de Panne. Deepmimic: Example-guided deep re- inforcement learning of physics-based character skills. ACM Trans. Graph., 37(4):143:1–143:14, July 2018. ISSN 0730-0301. doi: 10.1145/3197517.3201311. URL http://doi.acm.org/10.1145/3197517.3201311

    work page doi:10.1145/3197517.3201311 2018

  31. [31]

    AMP: Adversarial Motion Priors for Stylized Physics-Based Character Control.ACM Trans- actions on Graphics, 40(4):1–20, August 2021

    Xue Bin Peng, Ze Ma, Pieter Abbeel, Sergey Levine, and Angjoo Kanazawa. AMP: Adversarial Motion Priors for Stylized Physics-Based Character Control.ACM Trans- actions on Graphics, 40(4):1–20, August 2021. ISSN 0730-0301, 1557-7368. doi: 10.1145/3450626.3459670. URL http://arxiv.org/abs/2104.02180. arXiv:2104.02180 [cs]

    work page doi:10.1145/3450626.3459670 2021

  32. [32]

    A survey of temporal credit assignment in deep reinforcement learning, 2024

    Eduardo Pignatelli, Johan Ferret, Matthieu Geist, Thomas Mesnard, Hado van Hasselt, Olivier Pietquin, and Laura Toni. A Survey of Temporal Credit Assignment in Deep Reinforcement Learning, July 2024. URL http://arxiv. org/abs/2312.01072. arXiv:2312.01072 [cs]

    work page arXiv 2024

  33. [33]

    A Survey of Multi-Objective Sequential Decision-Making.Journal of Artificial Intel- ligence Research, 48:67–113, October 2013

    Diederik Marijn Roijers, Peter Vamplew, Shimon White- son, and Richard Dazeley. A Survey of Multi-Objective Sequential Decision-Making.Journal of Artificial Intel- ligence Research, 48:67–113, October 2013. ISSN 1076-

    work page 2013

  34. [34]

    URL http://arxiv.org/abs/ 1402.0590

    doi: 10.1613/jair.3987. URL http://arxiv.org/abs/ 1402.0590. arXiv:1402.0590 [cs]

    work page doi:10.1613/jair.3987

  35. [35]

    Efficient Reductions for Imitation Learning

    Stephane Ross and Drew Bagnell. Efficient Reductions for Imitation Learning. InProceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pages 661–668. JMLR Workshop and Confer- ence Proceedings, March 2010. URL https://proceedings. mlr.press/v9/ross10a.html. ISSN: 1938-7228

    work page 2010

  36. [36]

    Gordon, and J

    Stephane Ross, Geoffrey J. Gordon, and J. Andrew Bag- nell. A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning. March 2011. doi: 10.48550/arXiv.1011.0686. URL http://arxiv.org/ abs/1011.0686. arXiv:1011.0686 [cs]

    work page doi:10.48550/arxiv.1011.0686 2011

  37. [37]

    Roi- jers, and Ann Nowé

    Roxana R ˘adulescu, Patrick Mannion, Diederik M. Roi- jers, and Ann Nowé. Multi-Objective Multi-Agent De- cision Making: A Utility-based Analysis and Survey. Autonomous Agents and Multi-Agent Systems, 34(1): 10, April 2020. ISSN 1387-2532, 1573-7454. doi: 10.1007/s10458-019-09433-x. URL http://arxiv.org/abs/ 1909.02964. arXiv:1909.02964 [cs]

    work page doi:10.1007/s10458-019-09433-x 2020

  38. [38]

    Proximal Policy Optimization Algorithms

    John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal Policy Optimization Algorithms, August 2017. arXiv:1707.06347

    work page internal anchor Pith review Pith/arXiv arXiv 2017

  39. [39]

    Data-efficient reinforcement learning with self-predictive representations

    Max Schwarzer, Ankesh Anand, Rishab Goel, R. Devon Hjelm, Aaron Courville, and Philip Bachman. Data- Efficient Reinforcement Learning with Self-Predictive Representations, May 2021. URL http://arxiv.org/abs/ 2007.05929. arXiv:2007.05929 [cs]

    work page arXiv 2021

  40. [40]

    A Benchmark Comparison of Imitation Learning-based Control Policies for Autonomous Racing

    Xiatao Sun, Mingyan Zhou, Zhijun Zhuang, Shuo Yang, Johannes Betz, and Rahul Mangharam. A Benchmark Comparison of Imitation Learning-based Control Policies for Autonomous Racing. In2023 IEEE Intelligent Vehicles Symposium (IV), pages 1–5, June 2023. doi: 10. 1109/IV55152.2023.10186780. URL https://ieeexplore. ieee.org/abstract/document/10186780. ISSN: 2642-7214

    work page arXiv 2023

  41. [41]

    MEGA-DAgger: Imitation Learning with Multiple Imperfect Experts, May 2024

    Xiatao Sun, Shuo Yang, Mingyan Zhou, Kunpeng Liu, and Rahul Mangharam. MEGA-DAgger: Imitation Learning with Multiple Imperfect Experts, May 2024. URL http://arxiv.org/abs/2303.00638. arXiv:2303.00638 [cs]

    work page arXiv 2024

  42. [42]

    Understanding Self-Predictive Learn- ing for Reinforcement Learning

    Yunhao Tang, Zhaohan Daniel Guo, Pierre Harvey Richemond, Bernardo Ávila Pires, Yash Chandak, Rémi Munos, Mark Rowland, Mohammad Gheshlaghi Azar, Charline Le Lan, Clare Lyle, András György, Shantanu Thakoor, Will Dabney, Bilal Piot, Daniele Calandriello, and Michal Valko. Understanding Self-Predictive Learn- ing for Reinforcement Learning. December 2022. ...

    work page doi:10.48550/arxiv.2212.03319 2022

  43. [43]

    Robust player imitation using multiobjective evolution

    Niels van Hoorn, Julian Togelius, Daan Wierstra, and Jurgen Schmidhuber. Robust player imitation using multiobjective evolution. In2009 IEEE Congress on Evo- lutionary Computation, pages 652–659, May 2009. doi: 10.1109/CEC.2009.4983007. URL https://ieeexplore. ieee.org/document/4983007. ISSN: 1941-0026

    work page doi:10.1109/cec.2009.4983007 2009

  44. [44]

    When does Self-Prediction help? Understanding Auxiliary Tasks in Reinforcement Learning, June 2024

    Claas V oelcker, Tyler Kastner, Igor Gilitschenski, and Amir-massoud Farahmand. When does Self-Prediction help? Understanding Auxiliary Tasks in Reinforcement Learning, June 2024. URL http://arxiv.org/abs/2406. 17718. arXiv:2406.17718 [cs]

    work page arXiv 2024

  45. [45]

    Driving style imita- tion in simulated car racing using style evaluators and multi-objective evolution of a fuzzy logic controller

    Tsaipei Wang and Keng-Te Liaw. Driving style imita- tion in simulated car racing using style evaluators and multi-objective evolution of a fuzzy logic controller. In 2014 IEEE Conference on Norbert Wiener in the 21st Century (21CW), pages 1–7, June 2014. doi: 10.1109/ NORBERT.2014.6893872. URL https://ieeexplore.ieee. org/document/6893872. 15

    work page arXiv 2014

  46. [46]

    BeTAIL: Behavior Transformer Adversarial Imitation Learning from Human Racing Gameplay, July 2024

    Catherine Weaver, Chen Tang, Ce Hao, Kenta Kawamoto, Masayoshi Tomizuka, and Wei Zhan. BeTAIL: Behavior Transformer Adversarial Imitation Learning from Human Racing Gameplay, July 2024. URL http://arxiv.org/abs/2402.14194. arXiv:2402.14194 [cs]

    work page arXiv 2024

  47. [47]

    DeepRacing: Parame- terized Trajectories for Autonomous Racing, May 2020

    Trent Weiss and Madhur Behl. DeepRacing: Parame- terized Trajectories for Autonomous Racing, May 2020. URL http://arxiv.org/abs/2005.05178. arXiv:2005.05178 [cs]

    work page arXiv 2020

  48. [48]

    Wurman, Samuel Barrett, Kenta Kawamoto, James MacGlashan, Kaushik Subramanian, Thomas J

    Peter R. Wurman, Samuel Barrett, Kenta Kawamoto, James MacGlashan, Kaushik Subramanian, Thomas J. Walsh, Roberto Capobianco, Alisa Devlic, Franziska Eckert, Florian Fuchs, Leilani Gilpin, Piyush Khandel- wal, Varun Kompella, HaoChih Lin, Patrick MacAlpine, Declan Oller, Takuma Seno, Craig Sherstan, Michael D. Thomure, Houmehr Aghabozorgi, Leon Barrett, Ro...

    work page

  49. [49]

    doi: 10.1038/ s41586-021-04357-7

    ISSN 0028-0836, 1476-4687. doi: 10.1038/ s41586-021-04357-7. URL https://www.nature.com/ articles/s41586-021-04357-7

    work page

  50. [50]

    Auxiliary Tasks Speed Up Learning PointGoal Navigation, November 2020

    Joel Ye, Dhruv Batra, Erik Wijmans, and Abhishek Das. Auxiliary Tasks Speed Up Learning PointGoal Navigation, November 2020. URL http://arxiv.org/abs/ 2007.04561. arXiv:2007.04561 [cs]

    work page arXiv 2020