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 6851–6900 of 15113 papers

TitleStatusHype
Efficient Reinforcement Learning Experimentation in PyTorch—0
Computationally Efficient Reinforcement Learning: Targeted Exploration leveraging Simple Rules—0
Efficient Reinforcement Learning for Unsupervised Controlled Text Generation—0
Efficient Reinforcement Learning for High Dimensional Linear Quadratic Systems—0
Efficient Reinforcement Learning for Routing Jobs in Heterogeneous Queueing Systems—0
Efficient Reinforcement Learning from Demonstration Using Local Ensemble and Reparameterization with Split and Merge of Expert Policies—0
Efficient Reinforcement Learning in Deterministic Systems with Value Function Generalization—0
Efficient Reinforcement Learning in Factored MDPs with Application to Constrained RL—0
Efficient Reinforcement Learning in Resource Allocation Problems Through Permutation Invariant Multi-task Learning—0
Efficient Reinforcement Learning via Initial Pure Exploration—0
Extracting Heuristics from Large Language Models for Reward Shaping in Reinforcement Learning—0
Efficient Reinforcement Learning with Large Language Model Priors—0
Efficient Representation for Electric Vehicle Charging Station Operations using Reinforcement Learning—0
Efficient Representations of Object Geometry for Reinforcement Learning of Interactive Grasping Policies—0
Efficient Reservoir Management through Deep Reinforcement Learning—0
Efficient Residual Learning with Mixture-of-Experts for Universal Dexterous Grasping—0
Efficient Reinforcement Learning with Impaired Observability: Learning to Act with Delayed and Missing State Observations—0
Efficient Robotic Manipulation Through Offline-to-Online Reinforcement Learning and Goal-Aware State Information—0
Efficient Robotic Object Search via HIEM: Hierarchical Policy Learning with Intrinsic-Extrinsic Modeling—0
Efficient Robotic Task Generalization Using Deep Model Fusion Reinforcement Learning—0
Efficient Sampling-Based Maximum Entropy Inverse Reinforcement Learning with Application to Autonomous Driving—0
Efficient Scheduling of Data Augmentation for Deep Reinforcement Learning—0
Efficient Self-Supervised Data Collection for Offline Robot Learning—0
Efficient Skill Acquisition for Complex Manipulation Tasks in Obstructed Environments—0
POAR: Efficient Policy Optimization via Online Abstract State Representation Learning—0
Efficient statistical validation with edge cases to evaluate Highly Automated Vehicles—0
Efficient Stimuli Generation using Reinforcement Learning in Design Verification—0
Efficient Text-based Reinforcement Learning by Jointly Leveraging State and Commonsense Graph Representations—0
Efficient Text-based Reinforcement Learning by Jointly Leveraging State and Commonsense Graph Representations—0
Efficient Transformers: A Survey—0
Efficient Transformers in Reinforcement Learning using Actor-Learner Distillation—0
Efficient UAV Trajectory-Planning using Economic Reinforcement Learning—0
Efficient Use of heuristics for accelerating XCS-based Policy Learning in Markov Games—0
Efficient Wasserstein and Sinkhorn Policy Optimization—0
EgoMap: Projective mapping and structured egocentric memory for Deep RL—0
Ego-R1: Chain-of-Tool-Thought for Ultra-Long Egocentric Video Reasoning—0
Eigensubspace of Temporal-Difference Dynamics and How It Improves Value Approximation in Reinforcement Learning—0
Election of Collaborators via Reinforcement Learning for Federated Brain Tumor Segmentation—0
ELEMENTAL: Interactive Learning from Demonstrations and Vision-Language Models for Reward Design in Robotics—0
ELEMENT: Episodic and Lifelong Exploration via Maximum Entropy—0
Elements of Effective Deep Reinforcement Learning towards Tactical Driving Decision Making—0
Eliciting Reasoning in Language Models with Cognitive Tools—0
Eliciting User Preferences for Personalized Multi-Objective Decision Making through Comparative Feedback—0
E-MAPP: Efficient Multi-Agent Reinforcement Learning with Parallel Program Guidance—0
Embedding-Aligned Language Models—0
Embedding Safety into RL: A New Take on Trust Region Methods—0
Embed to Control Partially Observed Systems: Representation Learning with Provable Sample Efficiency—0
Embodied Learning for Lifelong Visual Perception—0
Embodied Visual Navigation with Automatic Curriculum Learning in Real Environments—0
Embracing advanced AI/ML to help investors achieve success: Vanguard Reinforcement Learning for Financial Goal Planning—0
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Benchmark Results

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