arXiv AI
Jul 9
ORCAID: Oblique Rule-Based Continuous-Action Interpretation for Deep RL Policies
arXiv:2607. 07235v1 Announce Type: cross Abstract: Explainability remains a key issue in reinforcement learning (RL).
Hierarchical Support Vector State Partitioning for Distilling Black Box Reinforcement Learning Policies
arXiv:2605. 04254v3 Announce Type: replace Abstract: We introduce State Vector Space Partitioning (SVSP), a novel method to mimic a black box reinforcement learning policy using a set of human-interpretable subpolicies.
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arXiv AI
3d ago
Diffusion-Augmented Markov Decision Processes for Maximum Entropy Reinforcement Learning
The paper introduces Diffusion-Augmented Markov Decision Processes (DA‑MDPs), a framework that extends Maximum Entropy Reinforcement Learning to diffusion-based policies. DA‑MDPs treat each reverse‑diffusion step as an RL decision, deriving a tractable reverse‑KL bound that decomposes across denoising transitions and yields diffusion‑augmented soft rewards, value functions, and policy objectives. The authors implement this framework with PPO, REPPO, and a maximum‑entropy WPO variant, showing improved continuous‑control performance, higher success rates on manipulation tasks, and memory‑efficient training with action chunking.
arXiv AI
Aug 26
On-policy Distillation with Verifiable Reward
arXiv:2608.24696v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) and on-policy distillation (OPD) have become two widely adopted paradigms for post-training lar...
arXiv AI
Jun 10
Fast and Highly Expressive Policy Learning for Offline Reinforcement Learning via Bootstrapped Flow Q-Learning
arXiv:2606. 10613v1 Announce Type: cross Abstract: Diffusion-based Q-learning has emerged as a powerful paradigm for offline reinforcement learning, but its reliance on multi-step denoising makes both training and inference computationally expensive and brittle.
arXiv Machine Learning
Jun 19
Approximate Next Policy Sampling: Replacing Conservative Target Policy Updates in Deep RL
arXiv:2605. 05481v2 Announce Type: replace Abstract: We revisit a classic "chicken-and-egg" problem in reinforcement learning: to safely improve a policy, the value function must be accurate on the state-visitation distribution of the updated policy.
arXiv Machine Learning
Aug 24
Decoupling Policy Extraction for Offline Reinforcement Learning
arXiv:2608.20909v1 Announce Type: new Abstract: Offline RL methods commonly jointly train the actor and critic, where the critic is used to guide the actor toward higher-value actions. This coupled l...
arXiv AI
Aug 11
Parameter Exploration for RLVR via Variational Learning
arXiv:2608. 09805v1 Announce Type: cross Abstract: Exploration has been a focus of reinforcement learning research for a long time.
arXiv AI
Jun 2
Interpretable Policy Distillation for Power Grid Topology Control
arXiv:2606. 00561v1 Announce Type: cross Abstract: Deep reinforcement learning (RL) offers a promising route to real-time power grid operation, yet large neural policies are costly to evaluate, hard to deploy on constrained hardware, and opaque to operators.
arXiv AI
Sep 10
Noisy-Space Policy Gradient for Diffusion Policies in Offline Reinforcement Learning
arXiv:2609.06882v1 Announce Type: cross Abstract: Diffusion policies offer a powerful and expressive parameterization for continuous control. Yet, their integration with reinforcement learning remain...
arXiv Machine Learning
Sep 18
Improving Online Reinforcement Learning via Bidirectional Behavior Prior Distillation
The paper introduces Bidirectional Behavior Prior Distillation (B2PD), a method that uses action‑value priors to train a conditional variational autoencoder for generating high‑value behavior support. These expert behavior priors are then distilled into the online reinforcement learning agent, reducing inefficient exploration and stabilizing policy updates. Experiments on state‑ and pixel‑based tasks show that B2PD improves sample efficiency while maintaining stable learning dynamics.
arXiv Machine Learning
2d ago
Role-Adaptive Policy Optimization for Offline Reinforcement Learning
arXiv:2609.40149v1 Announce Type: new Abstract: Policy regularization in offline reinforcement learning balances policy improvement against reliance on uncertain value estimates. This balance can dif...
arXiv AI
Jun 2
Value-Free Policy Optimization via Reward Partitioning
arXiv:2506. 13702v4 Announce Type: replace-cross Abstract: Single-trajectory preference optimization methods learn from datasets of ((prompt, response, reward)) tuples, offering a practical alternative to pairwise preference learning by directly leveraging scalar feedback.