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 2951–2975 of 15113 papers

TitleStatusHype
Optimizing Gene-Based Testing for Antibiotic Resistance Prediction—0
Uncertainty quantification for Markov chains with application to temporal difference learning—0
EPO: Explicit Policy Optimization for Strategic Reasoning in LLMs via Reinforcement Learning—0
A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models—0
Integrating Reinforcement Learning, Action Model Learning, and Numeric Planning for Tackling Complex TasksCode0
LocalEscaper: A Weakly-supervised Framework with Regional Reconstruction for Scalable Neural TSP Solvers—0
Demystifying Multilingual Chain-of-Thought in Process Reward Modeling—0
Navigating Demand Uncertainty in Container Shipping: Deep Reinforcement Learning for Enabling Adaptive and Feasible Master Stowage PlanningCode0
RAD: Training an End-to-End Driving Policy via Large-Scale 3DGS-based Reinforcement Learning—0
Learning Plasma Dynamics and Robust Rampdown Trajectories with Predict-First Experiments at TCV—0
FLAG-Trader: Fusion LLM-Agent with Gradient-based Reinforcement Learning for Financial Trading—0
FitLight: Federated Imitation Learning for Plug-and-Play Autonomous Traffic Signal Control—0
VLP: Vision-Language Preference Learning for Embodied Manipulation—0
Scaling Test-Time Compute Without Verification or RL is Suboptimal—0
Robot Deformable Object Manipulation via NMPC-generated Demonstrations in Deep Reinforcement Learning—0
Intersectional Fairness in Reinforcement Learning with Large State and Constraint Spaces—0
CAMEL: Continuous Action Masking Enabled by Large Language Models for Reinforcement Learning—0
Addressing Moral Uncertainty using Large Language Models for Ethical Decision-Making—0
Hovering Flight of Soft-Actuated Insect-Scale Micro Aerial Vehicles using Deep Reinforcement Learning—0
Evaluating the Paperclip Maximizer: Are RL-Based Language Models More Likely to Pursue Instrumental Goals?Code0
Scalable Multi-Agent Offline Reinforcement Learning and the Role of Information—0
Rule-Bottleneck Reinforcement Learning: Joint Explanation and Decision Optimization for Resource Allocation with Language Agents—0
Tackling the Zero-Shot Reinforcement Learning Loss Directly—0
BeamDojo: Learning Agile Humanoid Locomotion on Sparse Footholds—0
Reinforcement Learning in Strategy-Based and Atari Games: A Review of Google DeepMinds Innovations—0
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Benchmark Results

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