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 2271–2280 of 15113 papers

TitleStatusHype
Reinforcement Learning-based Recommender Systems with Large Language Models for State Reward and Action Modeling—0
Outcome-Constrained Large Language Models for Countering Hate Speech—0
Semantic-Aware Remote Estimation of Multiple Markov Sources Under Constraints—0
Planning with a Learned Policy Basis to Optimally Solve Complex Tasks—0
Distilling Reinforcement Learning Policies for Interpretable Robot Locomotion: Gradient Boosting Machines and Symbolic Regression—0
Policy Mirror Descent with LookaheadCode0
Constrained Reinforcement Learning with Smoothed Log Barrier Function—0
Heuristic Algorithm-based Action Masking Reinforcement Learning (HAAM-RL) with Ensemble Inference Method—0
Task-optimal data-driven surrogate models for eNMPC via differentiable simulation and optimization—0
Isometric Neural Machine Translation using Phoneme Count Ratio Reward-based Reinforcement Learning—0
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Benchmark Results

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