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 2631–2640 of 15113 papers

TitleStatusHype
RL-DAUNCE: Reinforcement Learning-Driven Data Assimilation with Uncertainty-Aware Constrained Ensembles—0
Taming OOD Actions for Offline Reinforcement Learning: An Advantage-Based Approach—0
Risk-sensitive Reinforcement Learning Based on Convex Scoring Functions—0
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
Large Language Models are Autonomous Cyber DefendersCode0
Fight Fire with Fire: Defending Against Malicious RL Fine-Tuning via Reward Neutralization—0
The Steganographic Potentials of Language Models—0
Deep Q-Network (DQN) multi-agent reinforcement learning (MARL) for Stock Trading—0
AMO: Adaptive Motion Optimization for Hyper-Dexterous Humanoid Whole-Body Control—0
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Benchmark Results

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