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 6701–6725 of 15113 papers

TitleStatusHype
Receding Horizon Inverse Reinforcement Learning—0
Quantum Policy Iteration via Amplitude Estimation and Grover Search -- Towards Quantum Advantage for Reinforcement Learning—0
Regret Analysis of Certainty Equivalence Policies in Continuous-Time Linear-Quadratic Systems—0
Overcoming the Spectral Bias of Neural Value Approximation—0
There is no Accuracy-Interpretability Tradeoff in Reinforcement Learning for Mazes—0
Deep Surrogate Assisted Generation of Environments—0
An Optimization Method-Assisted Ensemble Deep Reinforcement Learning Algorithm to Solve Unit Commitment Problems—0
Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-implementation GuidelinesCode0
Action Noise in Off-Policy Deep Reinforcement Learning: Impact on Exploration and Performance—0
A Study of Continual Learning Methods for Q-Learning—0
Learning to Generate Prompts for Dialogue Generation through Reinforcement Learning—0
Solving the Spike Feature Information Vanishing Problem in Spiking Deep Q Network with Potential Based Normalization—0
Scalable Online Disease Diagnosis via Multi-Model-Fused Actor-Critic Reinforcement Learning—0
Scalable Joint Learning of Wireless Multiple-Access Policies and their Signaling—0
Model-Based Reinforcement Learning for Offline Zero-Sum Markov Games—0
Sim2real for Reinforcement Learning Driven Next Generation Networks—0
Reinforced Inverse Scattering—0
On the Role of Discount Factor in Offline Reinforcement Learning—0
Variational Meta Reinforcement Learning for Social Robotics—0
MIX-MAB: Reinforcement Learning-based Resource Allocation Algorithm for LoRaWAN—0
On the Effectiveness of Fine-tuning Versus Meta-reinforcement Learning—0
Driving in Real Life with Inverse Reinforcement Learning—0
DeepTPI: Test Point Insertion with Deep Reinforcement LearningCode0
Discrete State-Action Abstraction via the Successor RepresentationCode0
Learning in Observable POMDPs, without Computationally Intractable Oracles—0
Show:102550
← PrevPage 269 of 605Next →

Benchmark Results

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