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 4111–4120 of 15113 papers

TitleStatusHype
CaT: Constraints as Terminations for Legged Locomotion Reinforcement Learning—0
From Two-Dimensional to Three-Dimensional Environment with Q-Learning: Modeling Autonomous Navigation with Reinforcement Learning and no LibrariesCode0
LORD: Large Models based Opposite Reward Design for Autonomous Driving—0
Learning the Optimal Power Flow: Environment Design MattersCode0
Depending on yourself when you should: Mentoring LLM with RL agents to become the master in cybersecurity games—0
Uncertainty-aware Distributional Offline Reinforcement Learning—0
Semantic-Aware Remote Estimation of Multiple Markov Sources Under Constraints—0
RL for Consistency Models: Faster Reward Guided Text-to-Image Generation—0
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
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Benchmark Results

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