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 3251–3275 of 15113 papers

TitleStatusHype
ScaleViz: Scaling Visualization Recommendation Models on Large Data—0
Accelerating Proximal Policy Optimization Learning Using Task Prediction for Solving Environments with Delayed Rewards—0
PROGRESSOR: A Perceptually Guided Reward Estimator with Self-Supervised Online Refinement—0
Free^2Guide: Gradient-Free Path Integral Control for Enhancing Text-to-Video Generation with Large Vision-Language Models—0
LLM-Based Offline Learning for Embodied Agents via Consistency-Guided Reward Ensemble—0
Unsupervised Event Outlier Detection in Continuous Time—0
Probing for Consciousness in Machines—0
M3: Mamba-assisted Multi-Circuit Optimization via MBRL with Effective Scheduling—0
Reinforcement learning-enhanced genetic algorithm for wind farm layout optimization—0
Broad Critic Deep Actor Reinforcement Learning for Continuous Control—0
From Laws to Motivation: Guiding Exploration through Law-Based Reasoning and Rewards—0
Learning a local trading strategy: deep reinforcement learning for grid-scale renewable energy integration—0
Segmenting Action-Value Functions Over Time-Scales in SARSA via TD(Δ)—0
Free Energy Projective Simulation (FEPS): Active inference with interpretability—0
Enhancing Molecular Design through Graph-based Topological Reinforcement Learning—0
Reward Fine-Tuning Two-Step Diffusion Models via Learning Differentiable Latent-Space Surrogate Reward—0
GraCo -- A Graph Composer for Integrated Circuits—0
Model Checking for Reinforcement Learning in Autonomous Driving: One Can Do More Than You Think!—0
Umbrella Reinforcement Learning -- computationally efficient tool for hard non-linear problemsCode0
Time-Scale Separation in Q-Learning: Extending TD() for Action-Value Function Decomposition—0
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
ACING: Actor-Critic for Instruction Learning in Black-Box Large Language ModelsCode0
Coarse-to-fine Q-Network with Action Sequence for Data-Efficient Robot Learning—0
GRL-Prompt: Towards Knowledge Graph based Prompt Optimization via Reinforcement Learning—0
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Benchmark Results

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