{"total":32,"rows":[{"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":"2307.03864","title":"When Do Transformers Shine in RL? Decoupling Memory from Credit Assignment","created":"2023-07-07","cited_by_count":63,"area":null},{"id":"2206.03446","title":"Learning in Observable POMDPs, without Computationally Intractable Oracles","created":"2022-06-07","cited_by_count":30,"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":"2211.01991","title":"Leveraging Fully Observable Policies for Learning under Partial Observability","created":"2022-11-03","cited_by_count":18,"area":null},{"id":"2309.17207","title":"Memory Gym: Towards Endless Tasks to Benchmark Memory Capabilities of Agents","created":"2023-09-29","cited_by_count":16,"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":"2307.08082","title":"POMDP inference and robust solution via deep reinforcement learning: An application to railway optimal maintenance","created":"2023-07-16","cited_by_count":13,"area":null},{"id":"2205.11104","title":"Generalization, Mayhems and Limits in Recurrent Proximal Policy Optimization","created":"2022-05-23","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":"2308.02594","title":"SMARLA: A Safety Monitoring Approach for Deep Reinforcement Learning Agents","created":"2023-08-03","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":"2212.02098","title":"A Machine with Short-Term, Episodic, and Semantic Memory Systems","created":"2022-12-05","cited_by_count":9,"area":null},{"id":"2210.00673","title":"Deep Learning for Wireless Networked Systems: a joint Estimation-Control-Scheduling Approach","created":"2022-10-03","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":"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":"2312.03263","title":"Weathering Ongoing Uncertainty: Learning and Planning in a Time-Varying Partially Observable Environment","created":"2023-12-06","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":"2211.01004","title":"Spatial-temporal recurrent reinforcement learning for autonomous ships","created":"2022-11-02","cited_by_count":3,"area":null},{"id":"2307.16769","title":"2-Level Reinforcement Learning for Ships on Inland Waterways: Path Planning and Following","created":"2023-07-25","cited_by_count":2,"area":null},{"id":"2305.00561","title":"Model-free Motion Planning of Autonomous Agents for Complex Tasks in Partially Observable Environments","created":"2023-04-30","cited_by_count":1,"area":null},{"id":"2307.15931","title":"Robust Recurrent Reinforcement Learning under Evolving Hidden Disturbances with Application to Rover Wheel Slip","created":"2023-07-29","cited_by_count":1,"area":null},{"id":"2312.14824","title":"An investigation of belief-free DRL and MCTS for inspection and maintenance planning","created":"2023-12-22","cited_by_count":1,"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":"2112.12465","title":"Missing Velocity in Dynamic Obstacle Avoidance based on Deep Reinforcement Learning","created":"2021-12-23","cited_by_count":0,"area":null},{"id":"2201.08020","title":"A Deep Learning Approach To Estimation Using Measurements Received Over a Network","created":"2022-01-20","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":"2302.08969","title":"Deep Reinforcement Learning for mmWave Initial Beam Alignment","created":"2023-02-17","cited_by_count":0,"area":null},{"id":"2304.10276","title":"Observer-Feedback-Feedforward Controller Structures in Reinforcement Learning","created":"2023-04-20","cited_by_count":0,"area":null},{"id":"2311.16841","title":"Two-step dynamic obstacle avoidance","created":"2023-11-28","cited_by_count":0,"area":null},{"id":"2507.11865","title":"Optimizing Drivers' Discount Order Acceptance Strategies: A Policy-Improved Deep Deterministic Policy Gradient Framework","created":"2025-07-16","cited_by_count":0,"area":null},{"id":"2609.31133","title":"AtomWorld-Mem: Memory-Restored World States for Long-Horizon Atomistic Evolution","created":"2026-09-25","cited_by_count":0,"area":null}],"next_cursor":null}