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 8151–8200 of 15113 papers

TitleStatusHype
You Only Evaluate Once: a Simple Baseline Algorithm for Offline RL—0
NaRLE: Natural Language Models using Reinforcement Learning with Emotion Feedback—0
Mining for Potent Inhibitors through Artificial Intelligence and Physics: A Unified Methodology for Ligand Based and Structure Based Drug Design—0
Reinforcement Learning for Admission Control in Wireless Virtual Network Embedding—0
Multi-Agent Path Planning Using Deep Reinforcement Learning—0
Learning to Assist Agents by Observing Them—0
Automating Privilege Escalation with Deep Reinforcement Learning—0
A Modified Q-Learning Algorithm for Rate-Profiling of Polarization Adjusted Convolutional (PAC) Codes—0
Hit and Lead Discovery with Explorative RL and Fragment-based Molecule Generation—0
Behaviour-conditioned policies for cooperative reinforcement learning tasks—0
Decentralized Safe Reinforcement Learning for Voltage Control—0
A Novel Automated Curriculum Strategy to Solve Hard Sokoban Planning Instances—0
DRL-Clusters: Buffer Management with Clustering based Deep Reinforcement Learning—0
Parallel Actors and Learners: A Framework for Generating Scalable RL Implementations—0
Meta-Reinforcement Learning via Buffering Graph Signatures for Live Video Streaming EventsCode0
Mapping Language to Programs using Multiple Reward Components with Inverse Reinforcement LearningCode0
Seeking Visual Discomfort: Curiosity-driven Representations for Reinforcement Learning—0
Feel-Good Thompson Sampling for Contextual Bandits and Reinforcement Learning—0
BRAC+: Improved Behavior Regularized Actor Critic for Offline Reinforcement LearningCode0
DNN-Opt: An RL Inspired Optimization for Analog Circuit Sizing using Deep Neural Networks—0
Guiding Evolutionary Strategies by Differentiable Robot SimulatorsCode0
A Cramér Distance perspective on Quantile Regression based Distributional Reinforcement LearningCode0
Cellular traffic offloading via Opportunistic Networking with Reinforcement Learning—0
Divergence-Regularized Multi-Agent Actor-Critic—0
Terminal Adaptive Guidance for Autonomous Hypersonic Strike Weapons via Reinforcement Learning—0
Safety aware model-based reinforcement learning for optimal control of a class of output-feedback nonlinear systems—0
Motion Planning for Autonomous Vehicles in the Presence of Uncertainty Using Reinforcement Learning—0
Multi-lane Cruising Using Hierarchical Planning and Reinforcement Learning—0
Is Policy Learning Overrated?: Width-Based Planning and Active Learning for AtariCode0
Stability Constrained Reinforcement Learning for Real-Time Voltage Control—0
Neural Network Verification in Control—0
Surveillance Evasion Through Bayesian Reinforcement LearningCode0
Modeling Interactions of Autonomous Vehicles and Pedestrians with Deep Multi-Agent Reinforcement Learning for Collision Avoidance—0
Trajectory Planning with Deep Reinforcement Learning in High-Level Action Spaces—0
Solving the Real Robot Challenge using Deep Reinforcement LearningCode0
Reinforcement Learning with Information-Theoretic Actuation—0
Reinforcement Learning for Classical Planning: Viewing Heuristics as Dense Reward Generators—0
Bitcoin Transaction Strategy Construction Based on Deep Reinforcement Learning—0
Decentralized Graph-Based Multi-Agent Reinforcement Learning Using Reward Machines—0
A Privacy-preserving Distributed Training Framework for Cooperative Multi-agent Deep Reinforcement Learning—0
Coordinated Reinforcement Learning for Optimizing Mobile Networks—0
HLIC: Harmonizing Optimization Metrics in Learned Image Compression by Reinforcement Learning—0
A Flexible Measurement of Diversity in Datasets with Random Network Distillation—0
Generalisation in Lifelong Reinforcement Learning through Logical Composition—0
Detecting Worst-case Corruptions via Loss Landscape Curvature in Deep Reinforcement Learning—0
Coordinated Attacks Against Federated Learning: A Multi-Agent Reinforcement Learning Approach—0
An Attempt to Model Human Trust with Reinforcement Learning—0
Fully Decentralized Model-based Policy Optimization with Networked Agents—0
Information-Bottleneck-Based Behavior Representation Learning for Multi-agent Reinforcement learning—0
Learning Pseudometric-based Action Representations for Offline Reinforcement Learning—0
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Benchmark Results

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