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

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Showing 3251–3260 of 15113 papers

TitleStatusHype
Dynamic Non-Prehensile Object Transport via Model-Predictive Reinforcement Learning—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
Accelerating Proximal Policy Optimization Learning Using Task Prediction for Solving Environments with Delayed Rewards—0
LLM-Based Offline Learning for Embodied Agents via Consistency-Guided Reward Ensemble—0
Probing for Consciousness in Machines—0
Unsupervised Event Outlier Detection in Continuous Time—0
M3: Mamba-assisted Multi-Circuit Optimization via MBRL with Effective Scheduling—0
Reinforcement learning-enhanced genetic algorithm for wind farm layout optimization—0
From Laws to Motivation: Guiding Exploration through Law-Based Reasoning and Rewards—0
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Benchmark Results

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