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 10401–10425 of 15113 papers

TitleStatusHype
Delta Schema Network in Model-based Reinforcement LearningCode0
Automatic Curriculum Learning through Value DisagreementCode1
Forgetful Experience Replay in Hierarchical Reinforcement Learning from DemonstrationsCode1
Reinforcement Learning with Uncertainty Estimation for Tactical Decision-Making in Intersections—0
Policy Evaluation and Seeking for Multi-Agent Reinforcement Learning via Best Response—0
Neural Ordinary Differential Equation Control of Dynamics on GraphsCode1
Parameterized MDPs and Reinforcement Learning Problems -- A Maximum Entropy Principle Based Framework—0
Agent Modelling under Partial Observability for Deep Reinforcement LearningCode1
Task-agnostic Exploration in Reinforcement Learning—0
ShieldNN: A Provably Safe NN Filter for Unsafe NN Controllers—0
Solving the Order Batching and Sequencing Problem using Deep Reinforcement Learning—0
COLREG-Compliant Collision Avoidance for Unmanned Surface Vehicle using Deep Reinforcement Learning—0
AWAC: Accelerating Online Reinforcement Learning with Offline DatasetsCode1
Index Selection for NoSQL Database with Deep Reinforcement Learning—0
Robot Perception enables Complex Navigation Behavior via Self-Supervised LearningCode1
Model-based Adversarial Meta-Reinforcement LearningCode1
Model Embedding Model-Based Reinforcement Learning—0
The Sample Complexity of Teaching-by-Reinforcement on Q-Learning—0
Parameter-Based Value FunctionsCode0
RL-CycleGAN: Reinforcement Learning Aware Simulation-To-Real—0
Preference-based Reinforcement Learning with Finite-Time Guarantees—0
Multi-Agent Reinforcement Learning for Adaptive User Association in Dynamic mmWave Networks—0
Online Reinforcement Learning Control by Direct Heuristic Dynamic Programming: from Time-Driven to Event-Driven—0
Reinforcement Learning Control of Robotic Knee with Human in the Loop by Flexible Policy Iteration—0
Multiagent Reinforcement Learning based Energy Beamforming ControlCode0
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Benchmark Results

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