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 2626–2650 of 15113 papers

TitleStatusHype
Reinforcement Learning for Game-Theoretic Resource Allocation on Graphs—0
Multi-agent Embodied AI: Advances and Future Directions—0
On Corruption-Robustness in Performative Reinforcement Learning—0
USPR: Learning a Unified Solver for Profiled RoutingCode0
Taming OOD Actions for Offline Reinforcement Learning: An Advantage-Based Approach—0
RL-DAUNCE: Reinforcement Learning-Driven Data Assimilation with Uncertainty-Aware Constrained Ensembles—0
Enhancing Reinforcement Learning for the Floorplanning of Analog ICs with Beam Search—0
Large Language Models are Autonomous Cyber DefendersCode0
Putting the Value Back in RL: Better Test-Time Scaling by Unifying LLM Reasoners With Verifiers—0
Extending a Quantum Reinforcement Learning Exploration Policy with Flags to Connect Four—0
Fight Fire with Fire: Defending Against Malicious RL Fine-Tuning via Reward Neutralization—0
Risk-sensitive Reinforcement Learning Based on Convex Scoring Functions—0
VLM Q-Learning: Aligning Vision-Language Models for Interactive Decision-Making—0
AMO: Adaptive Motion Optimization for Hyper-Dexterous Humanoid Whole-Body Control—0
The Steganographic Potentials of Language Models—0
Deep Q-Network (DQN) multi-agent reinforcement learning (MARL) for Stock Trading—0
Actor-Critics Can Achieve Optimal Sample Efficiency—0
Decentralized Distributed Proximal Policy Optimization (DD-PPO) for High Performance Computing Scheduling on Multi-User Systems—0
Automated Hybrid Reward Scheduling via Large Language Models for Robotic Skill Learning—0
Online Phase Estimation of Human Oscillatory Motions using Deep Learning—0
EMORL: Ensemble Multi-Objective Reinforcement Learning for Efficient and Flexible LLM Fine-TuningCode0
Exploring the Potential of Offline RL for Reasoning in LLMs: A Preliminary Study—0
Prompt-responsive Object Retrieval with Memory-augmented Student-Teacher Learning—0
Analytic Energy-Guided Policy Optimization for Offline Reinforcement Learning—0
A Generalised and Adaptable Reinforcement Learning Stopping MethodCode0
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Benchmark Results

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