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 11951–12000 of 15113 papers

TitleStatusHype
Robust Opponent Modeling via Adversarial Ensemble Reinforcement Learning in Asymmetric Imperfect-Information Games—0
ModelicaGym: Applying Reinforcement Learning to Modelica ModelsCode1
Segregation Dynamics with Reinforcement Learning and Agent Based Modeling—0
Sample Efficient Policy Gradient Methods with Recursive Variance ReductionCode0
Fine-Tuning Language Models from Human PreferencesCode3
A Hierarchical Two-tier Approach to Hyper-parameter Optimization in Reinforcement Learning—0
Stock market microstructure inference via multi-agent reinforcement learning—0
Controllable Length Control Neural Encoder-Decoder via Reinforcement Learning—0
Attraction-Repulsion Actor-Critic for Continuous Control Reinforcement Learning—0
Emergent Tool Use From Multi-Agent AutocurriculaCode2
Hierarchical Reinforcement Learning for Open-Domain Dialog—0
Generating Black-Box Adversarial Examples for Text Classifiers Using a Deep Reinforced Model—0
A Review of Tracking, Prediction and Decision Making Methods for Autonomous Driving—0
Adversarial Feature Training for Generalizable Robotic Visuomotor Control—0
MDP Playground: An Analysis and Debug Testbed for Reinforcement LearningCode0
Meta Reinforcement Learning for Sim-to-real Domain Adaptation—0
Off-road Autonomous Vehicles Traversability Analysis and Trajectory Planning Based on Deep Inverse Reinforcement Learning—0
Selective Network Discovery via Deep Reinforcement Learning on Embedded Spaces—0
Leveraging human Domain Knowledge to model an empirical Reward function for a Reinforcement Learning problem—0
Control Synthesis from Linear Temporal Logic Specifications using Model-Free Reinforcement LearningCode0
Data Centers Job Scheduling with Deep Reinforcement Learning—0
State Representation Learning from Demonstration—0
Wield: Systematic Reinforcement Learning With Progressive Randomization—0
Policy Prediction Network: Model-Free Behavior Policy with Model-Based Learning in Continuous Action Space—0
Model Based Planning with Energy Based Models—0
Driving in Dense Traffic with Model-Free Reinforcement LearningCode0
Learning to Recover Sparse Signals—0
Active Learning for Risk-Sensitive Inverse Reinforcement Learning—0
Flight Controller Synthesis Via Deep Reinforcement LearningCode0
Node Injection Attacks on Graphs via Reinforcement Learning—0
Towards an Adaptive Robot for Sports and Rehabilitation Coaching—0
Petri Net Machines for Human-Agent Interaction—0
Say What I Want: Towards the Dark Side of Neural Dialogue Models—0
Reinforcement Learning: a Comparison of UCB Versus Alternative Adaptive Policies—0
DL2: A Deep Learning-driven Scheduler for Deep Learning ClustersCode0
AITuning: Machine Learning-based Tuning Tool for Run-Time Communication Libraries—0
HJB Optimal Feedback Control with Deep Differential Value Functions and Action Constraints—0
Reinforcement Learning for Portfolio ManagementCode0
Joint Inference of Reward Machines and Policies for Reinforcement Learning—0
Efficiently Breaking the Curse of Horizon in Off-Policy Evaluation with Double Reinforcement Learning—0
Maximum Likelihood Constraint Inference for Inverse Reinforcement Learning—0
Modeling Sensorimotor Coordination as Multi-Agent Reinforcement Learning with Differentiable Communication—0
Modelling Working Memory using Deep Recurrent Reinforcement Learning—0
Reinforcement Learning Models of Human Behavior: Reward Processing in Mental Disorders—0
Mutual-Information Regularization in Markov Decision Processes and Actor-Critic Learning—0
Predicting optimal value functions by interpolating reward functions in scalarized multi-objective reinforcement learningCode0
RecSim: A Configurable Simulation Platform for Recommender SystemsCode0
Reinforcement Learning for Temporal Logic Control Synthesis with Probabilistic Satisfaction GuaranteesCode1
On Memory Mechanism in Multi-Agent Reinforcement Learning—0
Correlation Priors for Reinforcement Learning—0
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Benchmark Results

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