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 2561–2570 of 15113 papers

TitleStatusHype
The Distributional Reward Critic Framework for Reinforcement Learning Under Perturbed RewardsCode0
Improving Large Language Models via Fine-grained Reinforcement Learning with Minimum Editing ConstraintCode1
Parrot: Pareto-optimal Multi-Reward Reinforcement Learning Framework for Text-to-Image Generation—0
Reinforcement Learning for Optimizing RAG for Domain Chatbots—0
Innate-Values-driven Reinforcement Learning based Cooperative Multi-Agent Cognitive Modeling—0
Taming "data-hungry" reinforcement learning? Stability in continuous state-action spaces—0
An Information Theoretic Approach to Interaction-Grounded Learning—0
StarCraftImage: A Dataset For Prototyping Spatial Reasoning Methods For Multi-Agent Environments—0
Deep Reinforcement Learning for Multi-Truck Vehicle Routing Problems with Multi-Leg Demand Routes—0
Behavioural Cloning in VizDoom—0
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Benchmark Results

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