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 4901–4925 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
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Benchmark Results

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