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 5276–5300 of 15113 papers

TitleStatusHype
Sim-and-Real Reinforcement Learning for Manipulation: A Consensus-based Approach—0
Revolutionizing Genomics with Reinforcement Learning Techniques—0
Q-Cogni: An Integrated Causal Reinforcement Learning Framework—0
On Bellman's principle of optimality and Reinforcement learning for safety-constrained Markov decision process—0
Exponential Hardness of Reinforcement Learning with Linear Function Approximation—0
Limited Query Graph Connectivity Test—0
A Human-Centered Safe Robot Reinforcement Learning Framework with Interactive Behaviors—0
Leveraging Jumpy Models for Planning and Fast Learning in Robotic Domains—0
Logarithmic Switching Cost in Reinforcement Learning beyond Linear MDPs—0
AC2C: Adaptively Controlled Two-Hop Communication for Multi-Agent Reinforcement Learning—0
Finding Regularized Competitive Equilibria of Heterogeneous Agent Macroeconomic Models with Reinforcement Learning—0
GraphSR: A Data Augmentation Algorithm for Imbalanced Node Classification—0
Multi-Agent Reinforcement Learning with Common Policy for Antenna Tilt Optimization—0
VIPeR: Provably Efficient Algorithm for Offline RL with Neural Function ApproximationCode0
To the Noise and Back: Diffusion for Shared Autonomy—0
Concept Learning for Interpretable Multi-Agent Reinforcement Learning—0
Self-supervised network distillation: an effective approach to exploration in sparse reward environmentsCode0
Towards Decentralized Predictive Quality of Service in Next-Generation Vehicular Networks—0
Constrained Reinforcement Learning using Distributional Representation for Trustworthy Quadrotor UAV Tracking ControlCode0
Provably Efficient Reinforcement Learning via Surprise Bound—0
Learning to Play Text-based Adventure Games with Maximum Entropy Reinforcement LearningCode0
UAV Path Planning Employing MPC- Reinforcement Learning Method Considering Collision Avoidance—0
Minimax-Bayes Reinforcement LearningCode0
Provably Efficient Exploration in Quantum Reinforcement Learning with Logarithmic Worst-Case Regret—0
Reinforcement Learning for Block Decomposition of CAD Models—0
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Benchmark Results

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