arXiv AI
Jun 19
VOiLA: Vectorized Online Planning with Learned Diffusion Model for POMDP Agents
arXiv:2606. 19729v1 Announce Type: cross Abstract: Planning under uncertainty is an essential capability for autonomous robots.
From POMDP Theory to Deep RL with Particle Filters
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arXiv AI
Jul 17
VOiLA: Vectorized Online Planning with Learned Diffusion Models for POMDP Agents
arXiv:2606. 19729v2 Announce Type: replace-cross Abstract: Planning under uncertainty is an essential capability for autonomous robots.
arXiv AI
Sep 30
Risk-Averse Online POMDP Planning via CVaR of the Immediate Cost with Performance Guarantees
arXiv:2609.35874v1 Announce Type: new Abstract: Online POMDP planners optimize the expected cumulative cost, which can mask dangerous states when the belief places significant mass on high-cost state...
arXiv Machine Learning
Jul 24
Offline RL with Hierarchical Action Chunking
arXiv:2607. 20834v1 Announce Type: new Abstract: Offline goal-conditioned reinforcement learning (RL) holds the promise of learning general-purpose policies from static datasets.
Hugging Face Trending Papers
Jul 23
Offline RL with Hierarchical Action Chunking
Offline goal-conditioned reinforcement learning (RL) holds the promise of learning general-purpose policies from static datasets. However, scaling these methods to long-horizon tasks remains a challenge due to the curse of horizon, where value estimation errors can compound through long chains of bootstrapped Bellman backups.
arXiv AI
1d ago
VPFlow: Value-Predictive Planning with Imperfect World Models for Offline Goal Reaching
arXiv:2610.27280v1 Announce Type: cross Abstract: Offline goal-conditioned reinforcement learning enables skill acquisition by reaching any goal from any state without dense rewards. Model-based meth...
Hugging Face Trending Papers
Sep 23
Limiting-Kernel Q($λ$): Bridging Short and Long Horizons
In value-based reinforcement learning, improving the accuracy of policy evaluation has been shown to improve downstream policy optimization performance. The widely adopted family of approximations rel...
arXiv AI
Oct 15
Belief Embedding Tree Search
arXiv:2610.15046v1 Announce Type: new Abstract: Beliefs in a Partially Observable Markov Decision Process (POMDP) are posterior distributions over the hidden state given the agent's history of action...
arXiv Machine Learning
Jun 24
Posterior Sampling Reinforcement Learning with Gaussian Processes for Continuous Control: Sublinear Regret Bounds for Unbounded State Spaces
arXiv:2603. 08287v2 Announce Type: replace-cross Abstract: We analyze the Bayesian regret of the Gaussian process posterior sampling reinforcement learning (GP-PSRL) algorithm.
arXiv Machine Learning
Sep 24
Limiting-Kernel Q($\lambda$): Bridging Short and Long Horizons
Limiting‑Kernel Q(λ) (LKQL) is an off‑policy value estimator that blends n‑step truncation with a long‑horizon approximation based on the limiting kernel. It maintains the computational efficiency of n‑step methods while improving policy evaluation accuracy, especially for long‑horizon tasks. The authors prove faster convergence of LKQL’s operator under aperiodicity and near‑on‑policy conditions, and demonstrate empirical gains on MuJoCo continuous‑control benchmarks.
arXiv Machine Learning
Jun 3
Laplacian Representations for Decision-Time Planning
arXiv:2602. 05031v2 Announce Type: replace Abstract: Planning with a learned model remains a key challenge in model-based reinforcement learning (RL).
arXiv AI
Jul 21
SAGE: Subgoal-Conditioned Action Generation for Latent World Model Planning
arXiv:2607. 17973v1 Announce Type: new Abstract: Latent world models have emerged as a powerful planning paradigm by learning action-conditioned predictive dynamics and using them as internal simulators to imagine and evaluate candidate action sequences.