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 3626–3650 of 15113 papers

TitleStatusHype
Environment Transformer and Policy Optimization for Model-Based Offline Reinforcement Learning—0
Domain Randomization for Robust, Affordable and Effective Closed-loop Control of Soft Robots—0
Decoupling Skill Learning from Robotic Control for Generalizable Object Manipulation—0
adaPARL: Adaptive Privacy-Aware Reinforcement Learning for Sequential-Decision Making Human-in-the-Loop Systems—0
Graph Decision Transformer—0
Evolutionary Reinforcement Learning: A Survey—0
Learning Bipedal Walking for Humanoids with Current FeedbackCode3
On the Sample Complexity of Vanilla Model-Based Offline Reinforcement Learning with Dependent Samples—0
Dexterous In-hand Manipulation by Guiding Exploration with Simple Sub-skill Controllers—0
Efficient Skill Acquisition for Complex Manipulation Tasks in Obstructed Environments—0
Reinforcement Learning Based Self-play and State Stacking Techniques for Noisy Air Combat Environment—0
Perspectives on the Social Impacts of Reinforcement Learning with Human Feedback—0
Safe Reinforcement Learning via Probabilistic Logic ShieldsCode0
MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learning—0
Improved Sample Complexity Bounds for Distributionally Robust Reinforcement LearningCode0
Sparsity-Aware Intelligent Massive Random Access Control in Open RAN: A Reinforcement Learning Based Approach—0
Swim: A General-Purpose, High-Performing, and Efficient Activation Function for Locomotion Control TasksCode0
Ensemble Reinforcement Learning: A Survey—0
Bounding the Optimal Value Function in Compositional Reinforcement LearningCode0
Local Environment Poisoning Attacks on Federated Reinforcement Learning—0
CFlowNets: Continuous Control with Generative Flow NetworksCode0
Look-Ahead AC Optimal Power Flow: A Model-Informed Reinforcement Learning Approach—0
Double A3C: Deep Reinforcement Learning on OpenAI Gym Games—0
Wasserstein Actor-Critic: Directed Exploration via Optimism for Continuous-Actions Control—0
Neural Airport Ground HandlingCode1
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Benchmark Results

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