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 2901–2925 of 15113 papers

TitleStatusHype
Quantitative Resilience Modeling for Autonomous Cyber Defense—0
All Roads Lead to Likelihood: The Value of Reinforcement Learning in Fine-Tuning—0
What's Behind PPO's Collapse in Long-CoT? Value Optimization Holds the Secret—0
Accelerating Multi-Task Temporal Difference Learning under Low-Rank Representation—0
Active Alignments of Lens Systems with Reinforcement Learning—0
Adversarial Agents: Black-Box Evasion Attacks with Reinforcement Learning—0
Quality-Driven Curation of Remote Sensing Vision-Language Data via Learned Scoring Models—0
Minimax Optimal Reinforcement Learning with Quasi-Optimism—0
Scalable Reinforcement Learning for Virtual Machine Scheduling—0
Never too Prim to Swim: An LLM-Enhanced RL-based Adaptive S-Surface Controller for AUVs under Extreme Sea Conditions—0
Towards Understanding the Benefit of Multitask Representation Learning in Decision Process—0
Adaptive Reinforcement Learning for State Avoidance in Discrete Event Systems—0
Multimodal Dreaming: A Global Workspace Approach to World Model-Based Reinforcement Learning—0
Subtask-Aware Visual Reward Learning from Segmented Demonstrations—0
Hierarchical and Modular Network on Non-prehensile Manipulation in General Environments—0
Robust Gymnasium: A Unified Modular Benchmark for Robust Reinforcement Learning—0
AutoBS: Autonomous Base Station Deployment with Reinforcement Learning and Digital Network TwinsCode0
R1-T1: Fully Incentivizing Translation Capability in LLMs via Reasoning Learning—0
Improving the Efficiency of a Deep Reinforcement Learning-Based Power Management System for HPC Clusters Using Curriculum Learning—0
On the Importance of Reward Design in Reinforcement Learning-based Dynamic Algorithm Configuration: A Case Study on OneMax with (1+(λ,λ))-GACode0
Accelerating Model-Based Reinforcement Learning with State-Space World Models—0
CarPlanner: Consistent Auto-regressive Trajectory Planning for Large-scale Reinforcement Learning in Autonomous Driving—0
Efficient Reinforcement Learning by Guiding Generalist World Models with Non-Curated Data—0
Distill Not Only Data but Also Rewards: Can Smaller Language Models Surpass Larger Ones?—0
WOFOSTGym: A Crop Simulator for Learning Annual and Perennial Crop Management StrategiesCode0
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Benchmark Results

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