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 1351–1375 of 15113 papers

TitleStatusHype
Deterministic Exploration via Stationary Bellman Error Maximization—0
Local Linearity: the Key for No-regret Reinforcement Learning in Continuous MDPs—0
A Non-Monolithic Policy Approach of Offline-to-Online Reinforcement LearningCode0
Zonal RL-RRT: Integrated RL-RRT Path Planning with Collision Probability and Zone ConnectivityCode1
Reinforcement Learning Gradients as Vitamin for Online Finetuning Decision TransformersCode1
Teaching Embodied Reinforcement Learning Agents: Informativeness and Diversity of Language UseCode0
Stepping Out of the Shadows: Reinforcement Learning in Shadow Mode—0
SoftCTRL: Soft conservative KL-control of Transformer Reinforcement Learning for Autonomous Driving—0
Return Augmented Decision Transformer for Off-Dynamics Reinforcement Learning—0
Offline Behavior DistillationCode0
Kinetix: Investigating the Training of General Agents through Open-Ended Physics-Based Control TasksCode2
Self-Driving Car Racing: Application of Deep Reinforcement Learning—0
Offline Reinforcement Learning and Sequence Modeling for Downlink Link Adaptation—0
Online Intrinsic Rewards for Decision Making Agents from Large Language Model FeedbackCode1
Resource Governance in Networked Systems via Integrated Variational Autoencoders and Reinforcement Learning—0
PC-Gym: Benchmark Environments For Process Control ProblemsCode2
A Large Recurrent Action Model: xLSTM enables Fast Inference for Robotics TasksCode1
Learning Successor Features the Simple WayCode1
Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning—0
A Multi-Agent Reinforcement Learning Testbed for Cognitive Radio Applications—0
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
LongReward: Improving Long-context Large Language Models with AI FeedbackCode2
ODRL: A Benchmark for Off-Dynamics Reinforcement LearningCode2
FairStream: Fair Multimedia Streaming Benchmark for Reinforcement Learning AgentsCode0
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Benchmark Results

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