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 12101–12150 of 15113 papers

TitleStatusHype
Multi-task Learning and Catastrophic Forgetting in Continual Reinforcement LearningCode0
Deep Reinforcement Learning with Modulated Hebbian plus Q Network ArchitectureCode0
Leveraging Human Guidance for Deep Reinforcement Learning Tasks—0
Bayesian Optimization for Iterative LearningCode0
A Layered Architecture for Active Perception: Image Classification using Deep Reinforcement Learning—0
How Much Do Unstated Problem Constraints Limit Deep Robotic Reinforcement Learning?—0
Redirection Controller Using Reinforcement Learning—0
On the Convergence of Approximate and Regularized Policy Iteration Schemes—0
Meta-Inverse Reinforcement Learning with Probabilistic Context VariablesCode0
Robot Sound Interpretation: Combining Sight and Sound in Learning-Based Control—0
Instance-dependent _-bounds for policy evaluation in tabular reinforcement learning—0
MACS: Deep Reinforcement Learning based SDN Controller Synchronization Policy Design—0
Robust Opponent Modeling via Adversarial Ensemble Reinforcement Learning in Asymmetric Imperfect-Information Games—0
Visual Tracking by means of Deep Reinforcement Learning and an Expert Demonstrator—0
Segregation Dynamics with Reinforcement Learning and Agent Based Modeling—0
Sample Efficient Policy Gradient Methods with Recursive Variance ReductionCode0
Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environment—0
Dependency-Aware Computation Offloading in Mobile Edge Computing: A Reinforcement Learning Approach—0
A Hierarchical Two-tier Approach to Hyper-parameter Optimization in Reinforcement Learning—0
DeepGait: Planning and Control of Quadrupedal Gaits using Deep Reinforcement Learning—0
A Human-Centered Data-Driven Planner-Actor-Critic Architecture via Logic Programming—0
Controllable Length Control Neural Encoder-Decoder via Reinforcement Learning—0
A Review of Tracking, Prediction and Decision Making Methods for Autonomous Driving—0
Hierarchical Reinforcement Learning for Open-Domain Dialog—0
Attraction-Repulsion Actor-Critic for Continuous Control Reinforcement Learning—0
Adversarial Feature Training for Generalizable Robotic Visuomotor Control—0
Generating Black-Box Adversarial Examples for Text Classifiers Using a Deep Reinforced Model—0
Stock market microstructure inference via multi-agent reinforcement learning—0
MDP Playground: An Analysis and Debug Testbed for Reinforcement LearningCode0
Off-road Autonomous Vehicles Traversability Analysis and Trajectory Planning Based on Deep Inverse Reinforcement Learning—0
Meta Reinforcement Learning for Sim-to-real Domain Adaptation—0
Leveraging human Domain Knowledge to model an empirical Reward function for a Reinforcement Learning problem—0
Data Centers Job Scheduling with Deep Reinforcement Learning—0
Control Synthesis from Linear Temporal Logic Specifications using Model-Free Reinforcement LearningCode0
Selective Network Discovery via Deep Reinforcement Learning on Embedded Spaces—0
Driving in Dense Traffic with Model-Free Reinforcement LearningCode0
Policy Prediction Network: Model-Free Behavior Policy with Model-Based Learning in Continuous Action Space—0
State Representation Learning from Demonstration—0
Wield: Systematic Reinforcement Learning With Progressive Randomization—0
Model Based Planning with Energy Based Models—0
Node Injection Attacks on Graphs via Reinforcement Learning—0
Flight Controller Synthesis Via Deep Reinforcement LearningCode0
Learning to Recover Sparse Signals—0
Active Learning for Risk-Sensitive Inverse Reinforcement Learning—0
HJB Optimal Feedback Control with Deep Differential Value Functions and Action Constraints—0
DL2: A Deep Learning-driven Scheduler for Deep Learning ClustersCode0
AITuning: Machine Learning-based Tuning Tool for Run-Time Communication Libraries—0
Petri Net Machines for Human-Agent Interaction—0
Reinforcement Learning: a Comparison of UCB Versus Alternative Adaptive Policies—0
Say What I Want: Towards the Dark Side of Neural Dialogue Models—0
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Benchmark Results

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