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 8426–8450 of 15113 papers

TitleStatusHype
Optimal Cycling of a Heterogenous Battery Bank via Reinforcement Learning—0
WaveCorr: Correlation-savvy Deep Reinforcement Learning for Portfolio ManagementCode0
Towards optimized actions in critical situations of soccer games with deep reinforcement learningCode0
ROMAX: Certifiably Robust Deep Multiagent Reinforcement Learning via Convex Relaxation—0
Automatically Exposing Problems with Neural Dialog ModelsCode0
Dependability Analysis of Deep Reinforcement Learning based Robotics and Autonomous Systems through Probabilistic Model CheckingCode0
DSDF: An approach to handle stochastic agents in collaborative multi-agent reinforcement learning—0
Exploration in Deep Reinforcement Learning: From Single-Agent to Multiagent Domain—0
Few-shot Quality-Diversity OptimizationCode0
Continuous Homeostatic Reinforcement Learning for Self-Regulated Autonomous Agents—0
A Practical Adversarial Attack on Contingency Detection of Smart Energy Systems—0
Computation Rate Maximum for Mobile Terminals in UAV-assisted Wireless Powered MEC Networks with Fairness Constraint—0
Achieving Zero Constraint Violation for Constrained Reinforcement Learning via Primal-Dual Approach—0
RADARS: Memory Efficient Reinforcement Learning Aided Differentiable Neural Architecture Search—0
Theoretical Guarantees of Fictitious Discount Algorithms for Episodic Reinforcement Learning and Global Convergence of Policy Gradient Methods—0
Learning-to-defer for sequential medical decision-making under uncertainty—0
Reinforcement Learning for Load-balanced Parallel Particle Tracing—0
Concave Utility Reinforcement Learning with Zero-Constraint Violations—0
Direct Random Search for Fine Tuning of Deep Reinforcement Learning PoliciesCode0
EMVLight: A Decentralized Reinforcement Learning Framework for Efficient Passage of Emergency Vehicles—0
A Socially Aware Reinforcement Learning Agent for The Single Track Road Problem—0
HyAR: Addressing Discrete-Continuous Action Reinforcement Learning via Hybrid Action Representation—0
Federated Ensemble Model-based Reinforcement Learning in Edge Computing—0
Financial Trading with Feature Preprocessing and Recurrent Reinforcement Learning—0
Data Generation Method for Learning a Low-dimensional Safe Region in Safe Reinforcement Learning—0
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Benchmark Results

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