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 6251–6275 of 15113 papers

TitleStatusHype
Data efficient reinforcement learning and adaptive optimal perimeter control of network traffic dynamics—0
Skip Training for Multi-Agent Reinforcement Learning Controller for Industrial Wave Energy Converters—0
Unifying Causal Inference and Reinforcement Learning using Higher-Order Category Theory—0
Unified State Representation Learning under Data AugmentationCode0
Self-supervised Sequential Information Bottleneck for Robust Exploration in Deep Reinforcement Learning—0
Deterministic Sequencing of Exploration and Exploitation for Reinforcement Learning—0
Checklist Models for Improved Output Fluency in Piano Fingering Prediction—0
Pathfinding in Random Partially Observable Environments with Vision-Informed Deep Reinforcement Learning—0
Performance-Driven Controller Tuning via Derivative-Free Reinforcement Learning—0
Safe Reinforcement Learning with Contrastive Risk Prediction—0
Ask Before You Act: Generalising to Novel Environments by Asking QuestionsCode0
Cooperation and Competition: Flocking with Evolutionary Multi-Agent Reinforcement Learning—0
Task-Agnostic Learning to Accomplish New Tasks—0
An Analysis of Deep Reinforcement Learning Agents for Text-based Games—0
RASR: Risk-Averse Soft-Robust MDPs with EVaR and Entropic Risk—0
Robust Policy Optimization in Continuous-time Mixed H_2/H_ Stochastic Control—0
Reward Delay Attacks on Deep Reinforcement LearningCode0
Q-learning Decision Transformer: Leveraging Dynamic Programming for Conditional Sequence Modelling in Offline RL—0
Non-iterative generation of an optimal mesh for a blade passage using deep reinforcement learning—0
Hybrid Supervised and Reinforcement Learning for the Design and Optimization of Nanophotonic Structures—0
Adaptive Combination of a Genetic Algorithm and Novelty Search for Deep NeuroevolutionCode0
A Survey on Large-Population Systems and Scalable Multi-Agent Reinforcement Learning—0
An Empirical Evaluation of Posterior Sampling for Constrained Reinforcement LearningCode0
FORLORN: A Framework for Comparing Offline Methods and Reinforcement Learning for Optimization of RAN ParametersCode0
DC-MRTA: Decentralized Multi-Robot Task Allocation and Navigation in Complex Environments—0
Show:102550
← PrevPage 251 of 605Next →

Benchmark Results

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