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 5876–5900 of 15113 papers

TitleStatusHype
Doubly Inhomogeneous Reinforcement LearningCode0
Learning to Follow Instructions in Text-Based GamesCode0
Progress and summary of reinforcement learning on energy management of MPS-EV—0
Pretraining in Deep Reinforcement Learning: A Survey—0
Reinforcement Learning with Stepwise Fairness Constraints—0
Reward-Predictive Clustering—0
Wall Street Tree Search: Risk-Aware Planning for Offline Reinforcement Learning—0
ProtoX: Explaining a Reinforcement Learning Agent via PrototypingCode0
Spatio-temporal Incentives Optimization for Ride-hailing Services with Offline Deep Reinforcement Learning—0
Exposing Surveillance Detection Routes via Reinforcement Learning, Attack Graphs, and Cyber Terrain—0
Decentralized Federated Reinforcement Learning for User-Centric Dynamic TFDD Control—0
Benchmarking Quality-Diversity Algorithms on Neuroevolution for Reinforcement Learning—0
Diversity-based Deep Reinforcement Learning Towards Multidimensional Difficulty for Fighting Game AICode0
The Benefits of Model-Based Generalization in Reinforcement LearningCode0
Sensor Control for Information Gain in Dynamic, Sparse and Partially Observed Environments—0
Reinforcement Learning in Non-Markovian Environments—0
Theta-Resonance: A Single-Step Reinforcement Learning Method for Design Space Exploration—0
Oracle Inequalities for Model Selection in Offline Reinforcement Learning—0
lilGym: Natural Language Visual Reasoning with Reinforcement Learning—0
GEC: A Unified Framework for Interactive Decision Making in MDP, POMDP, and Beyond—0
Leveraging Fully Observable Policies for Learning under Partial ObservabilityCode0
A Survey on Reinforcement Learning in Aviation Applications—0
Contrastive Value Learning: Implicit Models for Simple Offline RL—0
Deep Reinforcement Learning for IRS Phase Shift Design in Spatiotemporally Correlated Environments—0
Learning to Grasp the Ungraspable with Emergent Extrinsic Dexterity—0
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Benchmark Results

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