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 4901–4950 of 15113 papers

TitleStatusHype
Deterministic Sequencing of Exploration and Exploitation for Reinforcement Learning—0
Checklist Models for Improved Output Fluency in Piano Fingering Prediction—0
Deep Reinforcement Learning for Cryptocurrency Trading: Practical Approach to Address Backtest OverfittingCode1
Unified State Representation Learning under Data AugmentationCode0
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
Robust Policy Optimization in Continuous-time Mixed H_2/H_ Stochastic Control—0
RASR: Risk-Averse Soft-Robust MDPs with EVaR and Entropic Risk—0
An Analysis of Deep Reinforcement Learning Agents for Text-based Games—0
Task-Agnostic Learning to Accomplish New Tasks—0
FORLORN: A Framework for Comparing Offline Methods and Reinforcement Learning for Optimization of RAN ParametersCode0
Hybrid Supervised and Reinforcement Learning for the Design and Optimization of Nanophotonic Structures—0
Non-iterative generation of an optimal mesh for a blade passage using deep reinforcement learning—0
Reward Delay Attacks on Deep Reinforcement LearningCode0
Q-learning Decision Transformer: Leveraging Dynamic Programming for Conditional Sequence Modelling in Offline RL—0
A Survey on Large-Population Systems and Scalable Multi-Agent Reinforcement Learning—0
Adaptive Combination of a Genetic Algorithm and Novelty Search for Deep NeuroevolutionCode0
Aerial View Localization with Reinforcement Learning: Towards Emulating Search-and-RescueCode1
An Empirical Evaluation of Posterior Sampling for Constrained Reinforcement LearningCode0
Hearts Gym: Learning Reinforcement Learning as a Team EventCode1
Concept-modulated model-based offline reinforcement learning for rapid generalization—0
Energy Optimization of Wind Turbines via a Neural Control Policy Based on Reinforcement Learning Markov Chain Monte Carlo Algorithm—0
Distilling Deep RL Models Into Interpretable Neuro-Fuzzy Systems—0
A SUMO Framework for Deep Reinforcement Learning Experiments Solving Electric Vehicle Charging Dispatching Problem—0
DC-MRTA: Decentralized Multi-Robot Task Allocation and Navigation in Complex Environments—0
On the Near-Optimality of Local Policies in Large Cooperative Multi-Agent Reinforcement Learning—0
A Deep Reinforcement Learning Strategy for UAV Autonomous Landing on a Platform—0
Finite-Time Error Bounds for Greedy-GQ—0
Annealing Optimization for Progressive Learning with Stochastic ApproximationCode0
Project proposal: A modular reinforcement learning based automated theorem proverCode0
SlateFree: a Model-Free Decomposition for Reinforcement Learning with Slate Actions—0
Reinforcement learning-based optimised control for tracking of nonlinear systems with adversarial attacks—0
Red Teaming with Mind Reading: White-Box Adversarial Policies Against RL AgentsCode0
Prediction Based Decision Making for Autonomous Highway Driving—0
Natural Policy Gradients In Reinforcement Learning Explained—0
Improving Assistive Robotics with Deep Reinforcement Learning—0
Variational Inference for Model-Free and Model-Based Reinforcement Learning—0
Statistical CSI-based Beamforming for RIS-Aided Multiuser MISO Systems using Deep Reinforcement Learning—0
Model-Free Deep Reinforcement Learning in Software-Defined Networks—0
Learning Practical Communication Strategies in Cooperative Multi-Agent Reinforcement Learning—0
Dialogue Evaluation with Offline Reinforcement Learning—0
TarGF: Learning Target Gradient Field to Rearrange Objects without Explicit Goal Specification—0
Taming Multi-Agent Reinforcement Learning with Estimator Variance Reduction—0
MetaTrader: An Reinforcement Learning Approach Integrating Diverse Policies for Portfolio Optimization—0
Systems Theoretic Process Analysis of a Run Time Assured Neural Network Control System—0
Transformers are Sample-Efficient World ModelsCode2
Actor Prioritized Experience ReplayCode1
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Benchmark Results

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