SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 8251–8300 of 15113 papers

TitleStatusHype
PFRL: Pose-Free Reinforcement Learning for 6D Pose Estimation—0
Phase Re-service in Reinforcement Learning Traffic Signal Control—0
Phasic Self-Imitative Reduction for Sparse-Reward Goal-Conditioned Reinforcement Learning—0
Phi-4-Mini-Reasoning: Exploring the Limits of Small Reasoning Language Models in Math—0
Phi-4-reasoning Technical Report—0
Phoebe: Reuse-Aware Online Caching with Reinforcement Learning for Emerging Storage Models—0
Phonetic-enriched Text Representation for Chinese Sentiment Analysis with Reinforcement Learning—0
Photonic architecture for reinforcement learning—0
pH-RL: A personalization architecture to bring reinforcement learning to health practice—0
Physically Plausible Full-Body Hand-Object Interaction Synthesis—0
Physical Simulation for Multi-agent Multi-machine Tending—0
Physics-Based Dexterous Manipulations with Estimated Hand Poses and Residual Reinforcement Learning—0
Physics Enhanced Residual Policy Learning (PERPL) for safety cruising in mixed traffic platooning under actuator and communication delay—0
Physics-Guided Hierarchical Reward Mechanism for Learning-Based Robotic Grasping—0
Physics-informed Actor-Critic for Coordination of Virtual Inertia from Power Distribution Systems—0
Physics-informed Dyna-Style Model-Based Deep Reinforcement Learning for Dynamic Control—0
Physics-Informed Machine Learning for Data Anomaly Detection, Classification, Localization, and Mitigation: A Review, Challenges, and Path Forward—0
Physics-informed Modularized Neural Network for Advanced Building Control by Deep Reinforcement Learning—0
Physics Instrument Design with Reinforcement Learning—0
PhysQ: A Physics Informed Reinforcement Learning Framework for Building Control—0
Closed Drafting as a Case Study for First-Principle Interpretability, Memory, and Generalizability in Deep Reinforcement Learning—0
PickLLM: Context-Aware RL-Assisted Large Language Model Routing—0
Fast TRAC: A Parameter-Free Optimizer for Lifelong Reinforcement Learning—0
PID Accelerated Temporal Difference Algorithms—0
PIP-Loco: A Proprioceptive Infinite Horizon Planning Framework for Quadrupedal Robot Locomotion—0
PI-QT-Opt: Predictive Information Improves Multi-Task Robotic Reinforcement Learning at Scale—0
pix2pockets: Shot Suggestions in 8-Ball Pool from a Single Image in the Wild—0
Pixel-Attentive Policy Gradient for Multi-Fingered Grasping in Cluttered Scenes—0
Pixel Reasoner: Incentivizing Pixel-Space Reasoning with Curiosity-Driven Reinforcement Learning—0
Placement in Integrated Circuits using Cyclic Reinforcement Learning and Simulated Annealing—0
Placement Optimization of Aerial Base Stations with Deep Reinforcement Learning—0
Placement Optimization with Deep Reinforcement Learning—0
Skill Reinforcement Learning and Planning for Open-World Long-Horizon Tasks—0
Plan, Attend, Generate: Character-Level Neural Machine Translation with Planning—0
Plan-Based Asymptotically Equivalent Reward Shaping—0
Plan-Based Relaxed Reward Shaping for Goal-Directed Tasks—0
Planning and Learning: Path-Planning for Autonomous Vehicles, a Review of the Literature—0
Planning and Learning with Stochastic Action Sets—0
Planning in Hierarchical Reinforcement Learning: Guarantees for Using Local Policies—0
Planning Irregular Object Packing via Hierarchical Reinforcement Learning—0
Planning to Practice: Efficient Online Fine-Tuning by Composing Goals in Latent Space—0
Planning to the Information Horizon of BAMDPs via Epistemic State Abstraction—0
Planning with Abstract Learned Models While Learning Transferable Subtasks—0
Planning with a Learned Policy Basis to Optimally Solve Complex Tasks—0
Planning with Exploration: Addressing Dynamics Bottleneck in Model-based Reinforcement Learning—0
Planning with RL and episodic-memory behavioral priors—0
Planning with Sequence Models through Iterative Energy Minimization—0
Epistemic Monte Carlo Tree Search—0
Plan-Seq-Learn: Language Model Guided RL for Solving Long Horizon Robotics Tasks—0
Plan-Space State Embeddings for Improved Reinforcement Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified