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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2551–2575 of 15113 papers

TitleStatusHype
Safe Reinforcement Learning with Free-form Natural Language Constraints and Pre-Trained Language Models—0
Reinforcement Learning from LLM Feedback to Counteract Goal Misgeneralization—0
Beyond Sparse Rewards: Enhancing Reinforcement Learning with Language Model Critique in Text Generation—0
Discovering Command and Control Channels Using Reinforcement Learning—0
BP(λ): Online Learning via Synthetic Gradients—0
UNEX-RL: Reinforcing Long-Term Rewards in Multi-Stage Recommender Systems with UNidirectional EXecution—0
Mutual Enhancement of Large Language and Reinforcement Learning Models through Bi-Directional Feedback Mechanisms: A Case Study—0
Model-Free Reinforcement Learning for Automated Fluid Administration in Critical Care—0
Improving Large Language Models via Fine-grained Reinforcement Learning with Minimum Editing ConstraintCode1
Interpretable Concept Bottlenecks to Align Reinforcement Learning AgentsCode1
Parrot: Pareto-optimal Multi-Reward Reinforcement Learning Framework for Text-to-Image Generation—0
The Distributional Reward Critic Framework for Reinforcement Learning Under Perturbed RewardsCode0
Optimistic Model Rollouts for Pessimistic Offline Policy Optimization—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
Long-term Safe Reinforcement Learning with Binary Feedback—0
Using reinforcement learning to improve drone-based inference of greenhouse gas fluxesCode0
NovelGym: A Flexible Ecosystem for Hybrid Planning and Learning Agents Designed for Open Worlds—0
On Sample-Efficient Offline Reinforcement Learning: Data Diversity, Posterior Sampling, and Beyond—0
Adaptive Discounting of Training Time Attacks—0
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Benchmark Results

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