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 3351–3375 of 15113 papers

TitleStatusHype
Future-conditioned Unsupervised Pretraining for Decision TransformerCode1
Distributional Reinforcement Learning with Dual Expectile-Quantile Regression—0
A Reminder of its Brittleness: Language Reward Shaping May Hinder Learning for Instruction Following AgentsCode0
Learning Interpretable Models of Aircraft Handling Behaviour by Reinforcement Learning from Human Feedback—0
Generating Synergistic Formulaic Alpha Collections via Reinforcement LearningCode3
Reward-Machine-Guided, Self-Paced Reinforcement LearningCode0
Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and MemoryCode2
DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion ModelsCode0
End-to-End Meta-Bayesian Optimisation with Transformer Neural ProcessesCode0
Deterministic policy gradient based optimal control with probabilistic constraints—0
Market Making with Deep Reinforcement Learning from Limit Order BooksCode1
PROTO: Iterative Policy Regularized Offline-to-Online Reinforcement LearningCode1
Decision-Aware Actor-Critic with Function Approximation and Theoretical GuaranteesCode0
Matrix Estimation for Offline Reinforcement Learning with Low-Rank Structure—0
Control invariant set enhanced safe reinforcement learning: improved sampling efficiency, guaranteed stability and robustness—0
SPRING: Studying the Paper and Reasoning to Play GamesCode1
Deep Reinforcement Learning with Plasticity Injection—0
A Mini Review on the utilization of Reinforcement Learning with OPC UA—0
Making Offline RL Online: Collaborative World Models for Offline Visual Reinforcement LearningCode1
Conditional Mutual Information for Disentangled Representations in Reinforcement LearningCode1
When should we prefer Decision Transformers for Offline Reinforcement Learning?Code1
Constrained Proximal Policy Optimization—0
Proximal Policy Gradient Arborescence for Quality Diversity Reinforcement Learning—0
ChemGymRL: An Interactive Framework for Reinforcement Learning for Digital Chemistry—0
Combining Multi-Objective Bayesian Optimization with Reinforcement Learning for TinyML—0
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Benchmark Results

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