Receding Horizon Inverse Reinforcement Learning

• URL: http://arxiv.org/abs/2206.04477v1
• Date: Thu, 9 Jun 2022 13:03:27 GMT
• Title: Receding Horizon Inverse Reinforcement Learning
• Abstract summary: Inverse reinforcement learning (IRL) seeks to infer a cost function that explains the underlying goals and preferences of expert demonstrations. This paper presents receding horizon inverse reinforcement learning (RHIRL), a new IRL algorithm for high-dimensional, noisy, continuous systems with black-box dynamic models.
• Score: 16.63162838890697
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Inverse reinforcement learning (IRL) seeks to infer a cost function that explains the underlying goals and preferences of expert demonstrations. This paper presents receding horizon inverse reinforcement learning (RHIRL), a new IRL algorithm for high-dimensional, noisy, continuous systems with black-box dynamic models. RHIRL addresses two key challenges of IRL: scalability and robustness. To handle high-dimensional continuous systems, RHIRL matches the induced optimal trajectories with expert demonstrations locally in a receding horizon manner and 'stitches' together the local solutions to learn the cost; it thereby avoids the 'curse of dimensionality'. This contrasts sharply with earlier algorithms that match with expert demonstrations globally over the entire high-dimensional state space. To be robust against imperfect expert demonstrations and system control noise, RHIRL learns a state-dependent cost function 'disentangled' from system dynamics under mild conditions. Experiments on benchmark tasks show that RHIRL outperforms several leading IRL algorithms in most instances. We also prove that the cumulative error of RHIRL grows linearly with the task duration.

Related papers

• Offline Reinforcement Learning with Universal Horizon Models [15.801661284310823]
  We introduce universal horizon models (UHM), a generalization of GHM that directly predicts future states under arbitrary horizons.<n>We propose a scalable value learning method that employs a winsorized horizon distribution to stabilize training by capping excessively large horizons.<n> Experimental results on 100 challenging OGBench tasks demonstrate that the proposed method outperforms competitive baselines.
  arXiv  Detail & Related papers  (2026-05-15T04:30:30Z)
• Scalable Exploration for High-Dimensional Continuous Control via Value-Guided Flow [12.08071565601655]
  We introduce Q-guided Flow Exploration (Qflex), a scalable reinforcement learning method that conducts exploration directly in the native high-dimensional action space.<n>During training, Qflex traverses actions from a learnable source distribution along a probability flow induced by the learned value function, aligning exploration with task-relevant gradients rather than isotropic noise.<n>Our results indicate that value-guided flows offer a principled and practical route to exploration at scale.
  arXiv  Detail & Related papers  (2026-01-27T15:30:10Z)
• Latent-Space Contrastive Reinforcement Learning for Stable and Efficient LLM Reasoning [16.244366307890832]
  We propose textbfDeepLatent Reasoning (DLR), a latent-space bidirectional contrastive reinforcement learning framework.<n>This framework shifts the trial-and-error cost from expensive token-level full sequence generation to the continuous latent manifold.<n> Experiments demonstrate that DLR achieves more stable training convergence, supports longer-horizon reasoning chains, and facilitates the sustainable accumulation of reasoning capabilities.
  arXiv  Detail & Related papers  (2026-01-24T03:18:22Z)
• Sample-Efficient Neurosymbolic Deep Reinforcement Learning [49.60927398960061]
  We propose a neuro-symbolic Deep RL approach that integrates background symbolic knowledge to improve sample efficiency.<n>Online reasoning is performed to guide the training process through two mechanisms.<n>We show improved performance over a state-of-the-art reward machine baseline.
  arXiv  Detail & Related papers  (2026-01-06T09:28:53Z)
• How LLMs Learn to Reason: A Complex Network Perspective [14.638878448692493]
  Training large language models with Reinforcement Learning from Verifiable Rewards exhibits a set of puzzling behaviors.<n>We propose that these seemingly disparate phenomena can be explained using a single unifying theory.<n>Our work provides a new physical intuition for engineering the emergent reasoning capabilities of future AI systems.
  arXiv  Detail & Related papers  (2025-09-28T04:10:37Z)
• Recursive Deep Inverse Reinforcement Learning [16.05411507856928]
  Inferring an adversary's goals from exhibited behavior is crucial for counterplanning and non-cooperative multi-agent systems. We propose an online Recursive Deep Inverse Reinforcement Learning (RDIRL) approach to recover the cost function governing the adversary actions and goals.
  arXiv  Detail & Related papers  (2025-04-17T17:39:35Z)
• RILe: Reinforced Imitation Learning [60.63173816209543]
  RILe (Reinforced Learning) is a framework that combines the strengths of imitation learning and inverse reinforcement learning to learn a dense reward function efficiently. Our framework produces high-performing policies in high-dimensional tasks where direct imitation fails to replicate complex behaviors.
  arXiv  Detail & Related papers  (2024-06-12T17:56:31Z)
• Bootstrapping Reinforcement Learning with Imitation for Vision-Based Agile Flight [20.92646531472541]
  We propose a novel approach that combines the performance of Reinforcement Learning (RL) and the sample efficiency of Imitation Learning (IL) Our framework contains three phases teacher policy using RL with privileged state information distilling it into a student policy via IL, and adaptive fine-tuning via RL. Tests show our approach can not only learn in scenarios where RL from scratch fails but also outperforms existing IL methods in both robustness and performance.
  arXiv  Detail & Related papers  (2024-03-18T19:25:57Z)
• Reasoning with Latent Diffusion in Offline Reinforcement Learning [11.349356866928547]
  offline reinforcement learning holds promise as a means to learn high-reward policies from a static dataset. Key challenge in offline RL lies in effectively stitching portions of suboptimal trajectories from the static dataset. We propose a novel approach that leverages the expressiveness of latent diffusion to model in-support trajectory sequences as compressed latent skills.
  arXiv  Detail & Related papers  (2023-09-12T20:58:21Z)
• CLARE: Conservative Model-Based Reward Learning for Offline Inverse Reinforcement Learning [26.05184273238923]
  This work aims to tackle a major challenge in offline Inverse Reinforcement Learning (IRL) We devise a principled algorithm (namely CLARE) that solves offline IRL efficiently via integrating "conservatism" into a learned reward function. Our theoretical analysis provides an upper bound on the return gap between the learned policy and the expert policy.
  arXiv  Detail & Related papers  (2023-02-09T17:16:29Z)
• Active Exploration for Inverse Reinforcement Learning [58.295273181096036]
  We propose a novel IRL algorithm: Active exploration for Inverse Reinforcement Learning (AceIRL) AceIRL actively explores an unknown environment and expert policy to quickly learn the expert's reward function and identify a good policy. We empirically evaluate AceIRL in simulations and find that it significantly outperforms more naive exploration strategies.
  arXiv  Detail & Related papers  (2022-07-18T14:45:55Z)
• IQ-Learn: Inverse soft-Q Learning for Imitation [95.06031307730245]
  imitation learning from a small amount of expert data can be challenging in high-dimensional environments with complex dynamics. Behavioral cloning is a simple method that is widely used due to its simplicity of implementation and stable convergence. We introduce a method for dynamics-aware IL which avoids adversarial training by learning a single Q-function.
  arXiv  Detail & Related papers  (2021-06-23T03:43:10Z)
• PlayVirtual: Augmenting Cycle-Consistent Virtual Trajectories for Reinforcement Learning [84.30765628008207]
  We propose a novel method, dubbed PlayVirtual, which augments cycle-consistent virtual trajectories to enhance the data efficiency for RL feature representation learning. Our method outperforms the current state-of-the-art methods by a large margin on both benchmarks.
  arXiv  Detail & Related papers  (2021-06-08T07:37:37Z)
• Self-Regression Learning for Blind Hyperspectral Image Fusion Without Label [11.291055330647977]
  We propose a self-regression learning method that reconstructs hyperspectral image (HSI) and estimate the observation model. In particular, we adopt an invertible neural network (INN) for restoring the HSI, and two fully-connected networks (FCN) for estimating the observation model. Our model can outperform the state-of-the-art methods in experiments on both synthetic and real-world dataset.
  arXiv  Detail & Related papers  (2021-03-31T04:48:21Z)
• Offline Reinforcement Learning from Images with Latent Space Models [60.69745540036375]
  offline reinforcement learning (RL) refers to the problem of learning policies from a static dataset of environment interactions. We build on recent advances in model-based algorithms for offline RL, and extend them to high-dimensional visual observation spaces. Our approach is both tractable in practice and corresponds to maximizing a lower bound of the ELBO in the unknown POMDP.
  arXiv  Detail & Related papers  (2020-12-21T18:28:17Z)
• Reset-Free Lifelong Learning with Skill-Space Planning [105.00539596788127]
  We propose Lifelong Skill Planning (LiSP), an algorithmic framework for non-episodic lifelong RL. LiSP learns the skills in an unsupervised manner using intrinsic rewards and plan over the learned skills using a learned dynamics model. We demonstrate empirically that LiSP successfully enables long-horizon planning and learns agents that can avoid catastrophic failures even in challenging non-stationary and non-episodic environments.
  arXiv  Detail & Related papers  (2020-12-07T09:33:02Z)
• Learning the Linear Quadratic Regulator from Nonlinear Observations [135.66883119468707]
  We introduce a new problem setting for continuous control called the LQR with Rich Observations, or RichLQR. In our setting, the environment is summarized by a low-dimensional continuous latent state with linear dynamics and quadratic costs. Our results constitute the first provable sample complexity guarantee for continuous control with an unknown nonlinearity in the system model and general function approximation.
  arXiv  Detail & Related papers  (2020-10-08T07:02:47Z)

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.