{"total":79,"rows":[{"id":"2302.14545","title":"Modern Bayesian Experimental Design","created":"2023-02-28","cited_by_count":125,"area":null},{"id":"2110.05038","title":"Recurrent Model-Free RL Can Be a Strong Baseline for Many POMDPs","created":"2021-10-11","cited_by_count":122,"area":null},{"id":"2210.00121","title":"Visuo-Tactile Transformers for Manipulation","created":"2022-09-30","cited_by_count":47,"area":null},{"id":"2202.03983","title":"Provable Reinforcement Learning with a Short-Term Memory","created":"2022-02-08","cited_by_count":40,"area":null},{"id":"2206.01078","title":"Deep Transformer Q-Networks for Partially Observable Reinforcement Learning","created":"2022-06-02","cited_by_count":36,"area":null},{"id":"2106.10316","title":"Proper Value Equivalence","created":"2021-06-18","cited_by_count":33,"area":null},{"id":"2110.06972","title":"Block Contextual MDPs for Continual Learning","created":"2021-10-13","cited_by_count":25,"area":null},{"id":"2110.15335","title":"Bayesian Sequential Optimal Experimental Design for Nonlinear Models Using Policy Gradient Reinforcement Learning","created":"2021-10-28","cited_by_count":24,"area":null},{"id":"2206.05266","title":"Does Self-supervised Learning Really Improve Reinforcement Learning from Pixels?","created":"2022-06-10","cited_by_count":24,"area":null},{"id":"2207.06415","title":"The Free Energy Principle for Perception and Action: A Deep Learning Perspective","created":"2022-07-13","cited_by_count":24,"area":null},{"id":"2201.12569","title":"Bellman Meets Hawkes: Model-Based Reinforcement Learning via Temporal Point Processes","created":"2022-01-29","cited_by_count":23,"area":null},{"id":"2110.12628","title":"Recurrent Off-policy Baselines for Memory-based Continuous Control","created":"2021-10-25","cited_by_count":21,"area":null},{"id":"2107.03635","title":"Sublinear Regret for Learning POMDPs","created":"2021-07-08","cited_by_count":19,"area":null},{"id":"2102.02274","title":"Neural Recursive Belief States in Multi-Agent Reinforcement Learning","created":"2021-02-03","cited_by_count":18,"area":null},{"id":"2106.10314","title":"Differentiable Particle Filtering without Modifying the Forward Pass","created":"2021-06-18","cited_by_count":18,"area":null},{"id":"2110.15949","title":"Sparsely Changing Latent States for Prediction and Planning in Partially Observable Domains","created":"2021-10-29","cited_by_count":18,"area":null},{"id":"2211.01991","title":"Leveraging Fully Observable Policies for Learning under Partial Observability","created":"2022-11-03","cited_by_count":18,"area":null},{"id":"2205.02589","title":"A Temporal-Pattern Backdoor Attack to Deep Reinforcement Learning","created":"2022-05-05","cited_by_count":16,"area":null},{"id":"2205.11051","title":"Flow-based Recurrent Belief State Learning for POMDPs","created":"2022-05-23","cited_by_count":16,"area":null},{"id":"2306.00249","title":"BetaZero: Belief-State Planning for Long-Horizon POMDPs using Learned Approximations","created":"2023-05-31","cited_by_count":14,"area":null},{"id":"2210.00999","title":"Latent State Marginalization as a Low-cost Approach for Improving Exploration","created":"2022-10-03","cited_by_count":13,"area":null},{"id":"2201.01247","title":"Value Functions Factorization with Latent State Information Sharing in Decentralized Multi-Agent Policy Gradients","created":"2022-01-04","cited_by_count":12,"area":null},{"id":"2206.05240","title":"ROI-Constrained Bidding via Curriculum-Guided Bayesian Reinforcement Learning","created":"2022-06-10","cited_by_count":12,"area":null},{"id":"2208.03520","title":"Recurrent networks, hidden states and beliefs in partially observable environments","created":"2022-08-06","cited_by_count":12,"area":null},{"id":"2210.05448","title":"A General Learning Framework for Open Ad Hoc Teamwork Using Graph-based Policy Learning","created":"2022-10-11","cited_by_count":12,"area":null},{"id":"2207.05631","title":"DGPO: Discovering Multiple Strategies with Diversity-Guided Policy Optimization","created":"2022-07-12","cited_by_count":10,"area":null},{"id":"2211.01595","title":"Reinforcement Learning in Non-Markovian Environments","created":"2022-11-03","cited_by_count":10,"area":null},{"id":"2305.18464","title":"Bridging the Sim-to-Real Gap from the Information Bottleneck Perspective","created":"2023-05-29","cited_by_count":10,"area":null},{"id":"2310.18591","title":"Inverse Decision Modeling: Learning Interpretable Representations of Behavior","created":"2023-10-28","cited_by_count":10,"area":null},{"id":"2311.12244","title":"Provable Representation with Efficient Planning for Partial Observable Reinforcement Learning","created":"2023-11-20","cited_by_count":10,"area":null},{"id":"2102.09677","title":"Training a Resilient Q-Network against Observational Interference","created":"2021-02-18","cited_by_count":9,"area":null},{"id":"2202.06557","title":"Reinforcement Learning in Presence of Discrete Markovian Context Evolution","created":"2022-02-14","cited_by_count":9,"area":null},{"id":"2303.03284","title":"The Wasserstein Believer: Learning Belief Updates for Partially Observable Environments through Reliable Latent Space Models","created":"2023-03-06","cited_by_count":9,"area":null},{"id":"2109.09876","title":"Context-Specific Representation Abstraction for Deep Option Learning","created":"2021-09-20","cited_by_count":8,"area":null},{"id":"2112.09456","title":"Compositional Learning-based Planning for Vision POMDPs","created":"2021-12-17","cited_by_count":8,"area":null},{"id":"2103.01350","title":"Hierarchical and Partially Observable Goal-driven Policy Learning with Goals Relational Graph","created":"2021-03-01","cited_by_count":7,"area":null},{"id":"2105.06228","title":"SIDE: State Inference for Partially Observable Cooperative Multi-Agent Reinforcement Learning","created":"2021-05-13","cited_by_count":7,"area":null},{"id":"2302.07350","title":"Graph schemas as abstractions for transfer learning, inference, and planning","created":"2023-02-14","cited_by_count":7,"area":null},{"id":"2309.14078","title":"ODE-based Recurrent Model-free Reinforcement Learning for POMDPs","created":"2023-09-25","cited_by_count":7,"area":null},{"id":"2311.06417","title":"Resolving uncertainty on the fly: Modeling adaptive driving behavior as active inference","created":"2023-11-10","cited_by_count":6,"area":null},{"id":"2203.16777","title":"Mask Atari for Deep Reinforcement Learning as POMDP Benchmarks","created":"2022-03-31","cited_by_count":5,"area":null},{"id":"2104.00827","title":"How Are Learned Perception-Based Controllers Impacted by the Limits of Robust Control?","created":"2021-04-02","cited_by_count":4,"area":null},{"id":"2304.05008","title":"Habits and goals in synergy: a variational Bayesian framework for behavior","created":"2023-04-11","cited_by_count":4,"area":null},{"id":"2307.05891","title":"PID-Inspired Inductive Biases for Deep Reinforcement Learning in Partially Observable Control Tasks","created":"2023-07-12","cited_by_count":4,"area":null},{"id":"2403.12417","title":"On Predictive planning and counterfactual learning in active inference","created":"2024-03-19","cited_by_count":4,"area":null},{"id":"2505.16732","title":"Sequential Monte Carlo for Policy Optimization in Continuous POMDPs","created":"2025-05-22","cited_by_count":4,"area":null},{"id":"2510.06540","title":"Scalable Policy-Based RL Algorithms for POMDPs","created":"2025-10-08","cited_by_count":4,"area":null},{"id":"2104.10986","title":"Reinforcement Learning using Guided Observability","created":"2021-04-22","cited_by_count":3,"area":null},{"id":"2103.13860","title":"Active Inference Tree Search in Large POMDPs","created":"2021-03-25","cited_by_count":2,"area":null},{"id":"2105.06660","title":"Estimating Disentangled Belief about Hidden State and Hidden Task for Meta-RL","created":"2021-05-14","cited_by_count":2,"area":null},{"id":"2206.11420","title":"PAC: Assisted Value Factorisation with Counterfactual Predictions in Multi-Agent Reinforcement Learning","created":"2022-06-22","cited_by_count":2,"area":null},{"id":"2311.07745","title":"Simplifying Complex Observation Models in Continuous POMDP Planning with Probabilistic Guarantees and Practice","created":"2023-11-13","cited_by_count":2,"area":null},{"id":"2410.03892","title":"Towards Cost Sensitive Decision Making","created":"2024-10-04","cited_by_count":2,"area":null},{"id":"2503.13438","title":"Deep Belief Markov Models for POMDP Inference","created":"2025-03-17","cited_by_count":2,"area":null},{"id":"2101.01883","title":"Off-Policy Meta-Reinforcement Learning Based on Feature Embedding Spaces","created":"2021-01-06","cited_by_count":1,"area":null},{"id":"2107.03015","title":"Evaluating the progress of Deep Reinforcement Learning in the real world: aligning domain-agnostic and domain-specific research","created":"2021-07-07","cited_by_count":1,"area":null},{"id":"2112.14657","title":"Dynamic programming with incomplete information to overcome navigational uncertainty in a nautical environment","created":"2021-12-29","cited_by_count":1,"area":null},{"id":"2202.08884","title":"BADDr: Bayes-Adaptive Deep Dropout RL for POMDPs","created":"2022-02-17","cited_by_count":1,"area":null},{"id":"2204.08573","title":"Training and Evaluation of Deep Policies using Reinforcement Learning and Generative Models","created":"2022-04-18","cited_by_count":1,"area":null},{"id":"2211.08234","title":"Build generally reusable agent-environment interaction models","created":"2022-11-13","cited_by_count":1,"area":null},{"id":"2212.08961","title":"Training Robots to Evaluate Robots: Example-Based Interactive Reward Functions for Policy Learning","created":"2022-12-17","cited_by_count":1,"area":null},{"id":"2304.10246","title":"Filter-Aware Model-Predictive Control","created":"2023-04-20","cited_by_count":1,"area":null},{"id":"2312.04597","title":"TrustFed: A Reliable Federated Learning Framework with Malicious-Attack Resistance","created":"2023-12-06","cited_by_count":1,"area":null},{"id":"2407.18812","title":"Online Planning in POMDPs with State-Requests","created":"2024-07-26","cited_by_count":1,"area":null},{"id":"2510.21107","title":"ESCORT: Efficient Stein-variational and Sliced Consistency-Optimized Temporal Belief Representation for POMDPs","created":"2025-10-24","cited_by_count":1,"area":null},{"id":"2602.05679","title":"Perception-Based Beliefs for POMDPs with Visual Observations","created":"2026-02-05","cited_by_count":1,"area":null},{"id":"2102.09208","title":"Learning Memory-Dependent Continuous Control from Demonstrations","created":"2021-02-18","cited_by_count":0,"area":null},{"id":"2112.05343","title":"Blockwise Sequential Model Learning for Partially Observable Reinforcement Learning","created":"2021-12-10","cited_by_count":0,"area":null},{"id":"2204.03525","title":"Temporal Alignment for History Representation in Reinforcement Learning","created":"2022-04-07","cited_by_count":0,"area":null},{"id":"2209.04999","title":"Multi-Step First: A Lightweight Deep Reinforcement Learning Strategy for Robust Continuous Control with Partial Observability","created":"2022-09-12","cited_by_count":0,"area":null},{"id":"2303.07397","title":"Fast exploration and learning of latent graphs with aliased observations","created":"2023-03-13","cited_by_count":0,"area":null},{"id":"2304.09769","title":"End-to-End Policy Gradient Method for POMDPs and Explainable Agents","created":"2023-04-19","cited_by_count":0,"area":null},{"id":"2307.01519","title":"Deep Attention Q-Network for Personalized Treatment Recommendation","created":"2023-07-04","cited_by_count":0,"area":null},{"id":"2308.14523","title":"Deep Reinforcement Learning for Uplink Scheduling in NOMA-URLLC Networks","created":"2023-08-28","cited_by_count":0,"area":null},{"id":"2308.14652","title":"Learning Visual Tracking and Reaching with Deep Reinforcement Learning on a UR10e Robotic Arm","created":"2023-08-28","cited_by_count":0,"area":null},{"id":"2402.01431","title":"Approximate Control for Continuous-Time POMDPs","created":"2024-02-02","cited_by_count":0,"area":null},{"id":"2604.03479","title":"Contextual Control without Memory Growth in a Context-Switching Task","created":"2026-04-03","cited_by_count":0,"area":null},{"id":"2605.14111","title":"Modeling Bounded Rationality in Drug Shortage Pharmacists Using Attention-Guided Dynamic Decomposition","created":"2026-05-13","cited_by_count":0,"area":null},{"id":"2605.14246","title":"Action-Conditioned Risk Gating for Safety-Critical Control under Partial Observability","created":"2026-05-14","cited_by_count":0,"area":null}],"next_cursor":null}