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 1251–1275 of 15113 papers

TitleStatusHype
Reinforcement learning-enhanced genetic algorithm for wind farm layout optimization—0
From Laws to Motivation: Guiding Exploration through Law-Based Reasoning and Rewards—0
Broad Critic Deep Actor Reinforcement Learning for Continuous Control—0
Learning a local trading strategy: deep reinforcement learning for grid-scale renewable energy integration—0
Reward Fine-Tuning Two-Step Diffusion Models via Learning Differentiable Latent-Space Surrogate Reward—0
Free Energy Projective Simulation (FEPS): Active inference with interpretability—0
Enhancing Molecular Design through Graph-based Topological Reinforcement Learning—0
Segmenting Action-Value Functions Over Time-Scales in SARSA via TD(Δ)—0
Natural Language Reinforcement LearningCode2
Multi-Agent Environments for Vehicle Routing ProblemsCode1
GraCo -- A Graph Composer for Integrated Circuits—0
Time-Scale Separation in Q-Learning: Extending TD() for Action-Value Function Decomposition—0
Model Checking for Reinforcement Learning in Autonomous Driving: One Can Do More Than You Think!—0
Marco-o1: Towards Open Reasoning Models for Open-Ended SolutionsCode5
Umbrella Reinforcement Learning -- computationally efficient tool for hard non-linear problemsCode0
A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback—0
Provably Efficient Action-Manipulation Attack Against Continuous Reinforcement Learning—0
GRL-Prompt: Towards Knowledge Graph based Prompt Optimization via Reinforcement Learning—0
LEDRO: LLM-Enhanced Design Space Reduction and Optimization for Analog CircuitsCode1
Coarse-to-fine Q-Network with Action Sequence for Data-Efficient Robot Learning—0
ACING: Actor-Critic for Instruction Learning in Black-Box Large Language ModelsCode0
Action-Attentive Deep Reinforcement Learning for Autonomous Alignment of BeamlinesCode0
Preserving Expert-Level Privacy in Offline Reinforcement Learning—0
Regret-Free Reinforcement Learning for LTL Specifications—0
Robust Reinforcement Learning under Diffusion Models for Data with Jumps—0
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Benchmark Results

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