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 4801–4825 of 15113 papers

TitleStatusHype
A Primal-Dual Algorithm for Offline Constrained Reinforcement Learning with Linear MDPs—0
A Primal-Dual-Critic Algorithm for Offline Constrained Reinforcement Learning—0
A Primer on Maximum Causal Entropy Inverse Reinforcement Learning—0
A Principled Permutation Invariant Approach to Mean-Field Multi-Agent Reinforcement Learning—0
A Privacy-preserving Distributed Training Framework for Cooperative Multi-agent Deep Reinforcement Learning—0
A Proposal: Interactively Learning to Summarise Timelines by Reinforcement Learning—0
A Provable Approach for End-to-End Safe Reinforcement Learning—0
A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning—0
A Provably Efficient Sample Collection Strategy for Reinforcement Learning—0
APT: Adaptive Perceptual quality based camera Tuning using reinforcement learning—0
A Quantum States Preparation Method Based on Difference-Driven Reinforcement Learning—0
AR3n: A Reinforcement Learning-based Assist-As-Needed Controller for Robotic Rehabilitation—0
A random measure approach to reinforcement learning in continuous time—0
Arbitrage of Energy Storage in Electricity Markets with Deep Reinforcement Learning—0
ARC -- Actor Residual Critic for Adversarial Imitation Learning—0
Arcades: A deep model for adaptive decision making in voice controlled smart-home—0
Architecting and Visualizing Deep Reinforcement Learning Models—0
A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards—0
A Real-World Quadrupedal Locomotion Benchmark for Offline Reinforcement Learning—0
Area-wide traffic signal control based on a deep graph Q-Network (DGQN) trained in an asynchronous manner—0
A Reduction Approach to Constrained Reinforcement Learning—0
A Reduction from Reinforcement Learning to No-Regret Online Learning—0
Are Gradient-based Saliency Maps Useful in Deep Reinforcement Learning?—0
A Behavior Regularized Implicit Policy for Offline Reinforcement Learning—0
A Regulation Enforcement Solution for Multi-agent Reinforcement Learning—0
Show:102550
← PrevPage 193 of 605Next →

Benchmark Results

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