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 901–925 of 15113 papers

TitleStatusHype
A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models—0
Navigating Demand Uncertainty in Container Shipping: Deep Reinforcement Learning for Enabling Adaptive and Feasible Master Stowage PlanningCode0
Addressing Moral Uncertainty using Large Language Models for Ethical Decision-Making—0
Learning Plasma Dynamics and Robust Rampdown Trajectories with Predict-First Experiments at TCV—0
Hovering Flight of Soft-Actuated Insect-Scale Micro Aerial Vehicles using Deep Reinforcement Learning—0
FitLight: Federated Imitation Learning for Plug-and-Play Autonomous Traffic Signal Control—0
Learning to Sample Effective and Diverse Prompts for Text-to-Image GenerationCode1
CAMEL: Continuous Action Masking Enabled by Large Language Models for Reinforcement Learning—0
Scaling Test-Time Compute Without Verification or RL is Suboptimal—0
Intersectional Fairness in Reinforcement Learning with Large State and Constraint Spaces—0
Robot Deformable Object Manipulation via NMPC-generated Demonstrations in Deep Reinforcement Learning—0
VLP: Vision-Language Preference Learning for Embodied Manipulation—0
FLAG-Trader: Fusion LLM-Agent with Gradient-based Reinforcement Learning for Financial Trading—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
Tackling the Zero-Shot Reinforcement Learning Loss Directly—0
Rule-Bottleneck Reinforcement Learning: Joint Explanation and Decision Optimization for Resource Allocation with Language Agents—0
Dynamic Reinforcement Learning for Actors—0
Provably Efficient RL under Episode-Wise Safety in Constrained MDPs with Linear Function Approximation—0
Causal Information Prioritization for Efficient Reinforcement Learning—0
Memory, Benchmark & Robots: A Benchmark for Solving Complex Tasks with Reinforcement LearningCode2
BeamDojo: Learning Agile Humanoid Locomotion on Sparse Footholds—0
Reinforcement Learning in Strategy-Based and Atari Games: A Review of Google DeepMinds Innovations—0
Digi-Q: Learning Q-Value Functions for Training Device-Control AgentsCode2
Diverse Transformer Decoding for Offline Reinforcement Learning Using Financial Algorithmic Approaches—0
Show:102550
← PrevPage 37 of 605Next →

Benchmark Results

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