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 4351–4360 of 15113 papers

TitleStatusHype
UNEX-RL: Reinforcing Long-Term Rewards in Multi-Stage Recommender Systems with UNidirectional EXecution—0
Optimistic Model Rollouts for Pessimistic Offline Policy Optimization—0
Parrot: Pareto-optimal Multi-Reward Reinforcement Learning Framework for Text-to-Image Generation—0
Model-Free Reinforcement Learning for Automated Fluid Administration in Critical Care—0
The Distributional Reward Critic Framework for Reinforcement Learning Under Perturbed RewardsCode0
Reinforcement Learning for Optimizing RAG for Domain Chatbots—0
Taming "data-hungry" reinforcement learning? Stability in continuous state-action spaces—0
An Information Theoretic Approach to Interaction-Grounded Learning—0
Innate-Values-driven Reinforcement Learning based Cooperative Multi-Agent Cognitive Modeling—0
StarCraftImage: A Dataset For Prototyping Spatial Reasoning Methods For Multi-Agent Environments—0
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Benchmark Results

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