First Order Model-Based RL through Decoupled Backpropagation

• URL: http://arxiv.org/abs/2509.00215v2
• Date: Thu, 04 Sep 2025 12:46:04 GMT
• Title: First Order Model-Based RL through Decoupled Backpropagation
• Authors: Joseph Amigo, Rooholla Khorrambakht, Elliot Chane-Sane, Nicolas Mansard, Ludovic Righetti,
• Abstract summary: We propose an approach that decouples trajectory generation from gradient computation.<n>Our method achieves the sample efficiency and speed of specialized locomotions such as SHAC.<n>We empirically validate our gradient algorithm on benchmark control tasks and demonstrate its effectiveness on a real Go2 quadruped robot.
• Score: 10.963895023346879
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: There is growing interest in reinforcement learning (RL) methods that leverage the simulator's derivatives to improve learning efficiency. While early gradient-based approaches have demonstrated superior performance compared to derivative-free methods, accessing simulator gradients is often impractical due to their implementation cost or unavailability. Model-based RL (MBRL) can approximate these gradients via learned dynamics models, but the solver efficiency suffers from compounding prediction errors during training rollouts, which can degrade policy performance. We propose an approach that decouples trajectory generation from gradient computation: trajectories are unrolled using a simulator, while gradients are computed via backpropagation through a learned differentiable model of the simulator. This hybrid design enables efficient and consistent first-order policy optimization, even when simulator gradients are unavailable, as well as learning a critic from simulation rollouts, which is more accurate. Our method achieves the sample efficiency and speed of specialized optimizers such as SHAC, while maintaining the generality of standard approaches like PPO and avoiding ill behaviors observed in other first-order MBRL methods. We empirically validate our algorithm on benchmark control tasks and demonstrate its effectiveness on a real Go2 quadruped robot, across both quadrupedal and bipedal locomotion tasks.

Related papers

• Offline Reinforcement Learning for End-to-End Autonomous Driving [1.2891210250935148]
  End-to-end (E2E) autonomous driving models take only camera images as input and directly predict a future trajectory.<n>Online reinforcement learning (RL) could mitigate IL-induced issues.<n>We introduce a camera-only E2E offline RL framework that performs no additional exploration and trains solely on a fixed simulator dataset.
  arXiv  Detail & Related papers  (2025-12-21T09:21:04Z)
• A Trainable Optimizer [18.195022468462753]
  We present a framework that jointly trains the full gradient estimator and the trainable weights of the model.<n>Pseudo-linear TO incurs negligible computational overhead, requiring only minimal additional multiplications.<n> Experiments demonstrate that TO methods converge faster than benchmark algorithms.
  arXiv  Detail & Related papers  (2025-08-03T14:06:07Z)
• Enhancing Training Data Attribution with Representational Optimization [57.61977909113113]
  Training data attribution methods aim to measure how training data impacts a model's predictions.<n>We propose AirRep, a representation-based approach that closes this gap by learning task-specific and model-aligned representations explicitly for TDA.<n>AirRep introduces two key innovations: a trainable encoder tuned for attribution quality, and an attention-based pooling mechanism that enables accurate estimation of group-wise influence.
  arXiv  Detail & Related papers  (2025-05-24T05:17:53Z)
• Revisiting the Initial Steps in Adaptive Gradient Descent Optimization [6.468625143772815]
  Adaptive gradient optimization methods, such as Adam, are prevalent in training deep neural networks across diverse machine learning tasks.<n>These methods often suffer from suboptimal generalization compared to descent gradient (SGD) and exhibit instability.<n>We introduce simple yet effective solutions: initializing the second-order moment estimation with non-zero values.
  arXiv  Detail & Related papers  (2024-12-03T04:28:14Z)
• Autonomous Vehicle Controllers From End-to-End Differentiable Simulation [60.05963742334746]
  We propose a differentiable simulator and design an analytic policy gradients (APG) approach to training AV controllers. Our proposed framework brings the differentiable simulator into an end-to-end training loop, where gradients of environment dynamics serve as a useful prior to help the agent learn a more grounded policy. We find significant improvements in performance and robustness to noise in the dynamics, as well as overall more intuitive human-like handling.
  arXiv  Detail & Related papers  (2024-09-12T11:50:06Z)
• Learning on Transformers is Provable Low-Rank and Sparse: A One-layer Analysis [63.66763657191476]
  We show that efficient numerical training and inference algorithms as low-rank computation have impressive performance for learning Transformer-based adaption. We analyze how magnitude-based models affect generalization while improving adaption. We conclude that proper magnitude-based has a slight on the testing performance.
  arXiv  Detail & Related papers  (2024-06-24T23:00:58Z)
• Adaptive Horizon Actor-Critic for Policy Learning in Contact-Rich Differentiable Simulation [36.308936312224404]
  This paper introduces Adaptive Horizon Actor-Critic (AHAC), an FO-MBRL algorithm that reduces gradient error by adapting the model-based horizon to avoid stiff dynamics. Empirical findings reveal that AHAC outperforms MFRL baselines, attaining 40% more reward across a set of locomotion tasks and efficiently scaling to high-dimensional control environments with improved wall-clock-time efficiency.
  arXiv  Detail & Related papers  (2024-05-28T03:28:00Z)
• Adaptive Federated Learning Over the Air [108.62635460744109]
  We propose a federated version of adaptive gradient methods, particularly AdaGrad and Adam, within the framework of over-the-air model training. Our analysis shows that the AdaGrad-based training algorithm converges to a stationary point at the rate of $mathcalO( ln(T) / T 1 - frac1alpha ).
  arXiv  Detail & Related papers  (2024-03-11T09:10:37Z)
• Accelerated Policy Learning with Parallel Differentiable Simulation [59.665651562534755]
  We present a differentiable simulator and a new policy learning algorithm (SHAC) Our algorithm alleviates problems with local minima through a smooth critic function. We show substantial improvements in sample efficiency and wall-clock time over state-of-the-art RL and differentiable simulation-based algorithms.
  arXiv  Detail & Related papers  (2022-04-14T17:46:26Z)
• Differentiable Agent-Based Simulation for Gradient-Guided Simulation-Based Optimization [0.0]
  gradient estimation methods can be used to steer the optimization towards a local optimum. In traffic signal timing optimization problems with high input dimension, the gradient-based methods exhibit substantially superior performance.
  arXiv  Detail & Related papers  (2021-03-23T11:58:21Z)
• Extrapolation for Large-batch Training in Deep Learning [72.61259487233214]
  We show that a host of variations can be covered in a unified framework that we propose. We prove the convergence of this novel scheme and rigorously evaluate its empirical performance on ResNet, LSTM, and Transformer.
  arXiv  Detail & Related papers  (2020-06-10T08:22:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.