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 3351–3400 of 15113 papers

TitleStatusHype
Maximum Entropy Hindsight Experience Replay—0
Teaching Embodied Reinforcement Learning Agents: Informativeness and Diversity of Language UseCode0
A Non-Monolithic Policy Approach of Offline-to-Online Reinforcement LearningCode0
Deterministic Exploration via Stationary Bellman Error Maximization—0
Offline Reinforcement Learning and Sequence Modeling for Downlink Link Adaptation—0
Stepping Out of the Shadows: Reinforcement Learning in Shadow Mode—0
Resource Governance in Networked Systems via Integrated Variational Autoencoders and Reinforcement Learning—0
Self-Driving Car Racing: Application of Deep Reinforcement Learning—0
Offline Behavior DistillationCode0
SoftCTRL: Soft conservative KL-control of Transformer Reinforcement Learning for Autonomous Driving—0
Return Augmented Decision Transformer for Off-Dynamics Reinforcement Learning—0
Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning—0
A Multi-Agent Reinforcement Learning Testbed for Cognitive Radio Applications—0
Getting By Goal Misgeneralization With a Little Help From a Mentor—0
FairStream: Fair Multimedia Streaming Benchmark for Reinforcement Learning AgentsCode0
The Limits of Transfer Reinforcement Learning with Latent Low-rank Structure—0
Bilevel Model for Electricity Market Mechanism Optimisation via Quantum Computing Enhanced Reinforcement Learning—0
Off-Policy Selection for Initiating Human-Centric Experimental Design—0
GFlowNet Fine-tuning for Diverse Correct Solutions in Mathematical Reasoning Tasks—0
Beyond Simple Sum of Delayed Rewards: Non-Markovian Reward Modeling for Reinforcement Learning—0
Reinforcement Learning for Aligning Large Language Models Agents with Interactive Environments: Quantifying and Mitigating Prompt Overfitting—0
Random Policy Enables In-Context Reinforcement Learning within Trust Horizons—0
AgentForge: A Flexible Low-Code Platform for Reinforcement Learning Agent DesignCode0
On-Robot Reinforcement Learning with Goal-Contrastive Rewards—0
Adversarial Environment Design via Regret-Guided Diffusion Models—0
Provably Adaptive Average Reward Reinforcement Learning for Metric Spaces—0
Humanizing the Machine: Proxy Attacks to Mislead LLM Detectors—0
SAMG: State-Action-Aware Offline-to-Online Reinforcement Learning with Offline Model Guidance—0
PointPatchRL -- Masked Reconstruction Improves Reinforcement Learning on Point Clouds—0
Learn 2 Rage: Experiencing The Emotional Roller Coaster That Is Reinforcement Learning—0
Primal-Dual Spectral Representation for Off-policy Evaluation—0
Optimizing Load Scheduling in Power Grids Using Reinforcement Learning and Markov Decision Processes—0
Dynamic Spectrum Access for Ambient Backscatter Communication-assisted D2D Systems with Quantum Reinforcement Learning—0
Learning Versatile Skills with Curriculum MaskingCode0
The Hive Mind is a Single Reinforcement Learning Agent—0
Process Supervision-Guided Policy Optimization for Code Generation—0
Meta Stackelberg Game: Robust Federated Learning against Adaptive and Mixed Poisoning Attacks—0
Survival of the Fittest: Evolutionary Adaptation of Policies for Environmental Shifts—0
DROP: Distributional and Regular Optimism and Pessimism for Reinforcement Learning—0
Exploring RL-based LLM Training for Formal Language Tasks with Programmed RewardsCode0
Episodic Future Thinking Mechanism for Multi-agent Reinforcement Learning—0
DyPNIPP: Predicting Environment Dynamics for RL-based Robust Informative Path Planning—0
Multi-Modal Transformer and Reinforcement Learning-based Beam Management—0
Curriculum Reinforcement Learning for Complex Reward Functions—0
Benchmarking Smoothness and Reducing High-Frequency Oscillations in Continuous Control Policies—0
Offline reinforcement learning for job-shop scheduling problems—0
Reinforcement Learning for Dynamic Memory AllocationCode0
Training Language Models to Critique With Multi-agent Feedback—0
MENTOR: Mixture-of-Experts Network with Task-Oriented Perturbation for Visual Reinforcement Learning—0
Augmented Lagrangian-Based Safe Reinforcement Learning Approach for Distribution System Volt/VAR Control—0
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Benchmark Results

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