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 4926–4950 of 15113 papers

TitleStatusHype
A Scalable Reinforcement Learning Approach for Attack Allocation in Swarm to Swarm Engagement Problems—0
A Scalable Reinforcement Learning-based System Using On-Chain Data for Cryptocurrency Portfolio Management—0
A Scale-Independent Multi-Objective Reinforcement Learning with Convergence Analysis—0
A Secure Learning Control Strategy via Dynamic Camouflaging for Unknown Dynamical Systems under Attacks—0
A Sensorimotor Reinforcement Learning Framework for Physical Human-Robot Interaction—0
A storage expansion planning framework using reinforcement learning and simulation-based optimization—0
ASHA: Assistive Teleoperation via Human-in-the-Loop Reinforcement Learning—0
A Sharp Analysis of Model-based Reinforcement Learning with Self-Play—0
A Complete Characterization of Linear Estimators for Offline Policy Evaluation—0
A Short Note on Soft-max and Policy Gradients in Bandits Problems—0
A Short Note on the Relationship of Information Gain and Eluder Dimension—0
A Short Survey On Memory Based Reinforcement Learning—0
A Short Survey on Probabilistic Reinforcement Learning—0
A short variational proof of equivalence between policy gradients and soft Q learning—0
A Shoulder to Cry on: Towards A Motivational Virtual Assistant for Assuaging Mental Agony—0
A Signaling Game Approach to Databases Querying and Interaction—0
A Simple Imitation Learning Method via Contrastive Regularization—0
A Simple Reinforcement Learning Mechanism for Resource Allocation in LTE-A Networks with Markov Decision Process and Q-Learning—0
A Simple Reward-free Approach to Constrained Reinforcement Learning—0
A Simple Sparse Denoising Layer for Robust Deep Learning—0
A Distance-based Anomaly Detection Framework for Deep Reinforcement Learning—0
A Simple Unified Uncertainty-Guided Framework for Offline-to-Online Reinforcement Learning—0
Novelty Detection in Reinforcement Learning with World Models—0
A Single-Timescale Analysis For Stochastic Approximation With Multiple Coupled Sequences—0
Ask1: Development and Reinforcement Learning-Based Control of a Custom Quadruped Robot—0
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Benchmark Results

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