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 5226–5250 of 15113 papers

TitleStatusHype
Improved Sample Complexity Bounds for Distributionally Robust Reinforcement LearningCode0
Ensemble Reinforcement Learning: A Survey—0
Local Environment Poisoning Attacks on Federated Reinforcement Learning—0
Bounding the Optimal Value Function in Compositional Reinforcement LearningCode0
Look-Ahead AC Optimal Power Flow: A Model-Informed Reinforcement Learning Approach—0
CFlowNets: Continuous Control with Generative Flow NetworksCode0
Double A3C: Deep Reinforcement Learning on OpenAI Gym Games—0
Wasserstein Actor-Critic: Directed Exploration via Optimism for Continuous-Actions Control—0
Toward Risk-based Optimistic Exploration for Cooperative Multi-Agent Reinforcement Learning—0
Tile Networks: Learning Optimal Geometric Layout for Whole-page Recommendation—0
RePreM: Representation Pre-training with Masked Model for Reinforcement Learning—0
Guarded Policy Optimization with Imperfect Online Demonstrations—0
Intelligent O-RAN Traffic Steering for URLLC Through Deep Reinforcement Learning—0
Hindsight States: Blending Sim and Real Task Elements for Efficient Reinforcement Learning—0
Approximating Energy Market Clearing and Bidding With Model-Based Reinforcement Learning—0
Learning to Influence Human Behavior with Offline Reinforcement Learning—0
Co-learning Planning and Control Policies Constrained by Differentiable Logic Specifications—0
Domain Adaptation of Reinforcement Learning Agents based on Network Service Proximity—0
Data-efficient, Explainable and Safe Box Manipulation: Illustrating the Advantages of Physical Priors in Model-Predictive Control—0
Expert-Free Online Transfer Learning in Multi-Agent Reinforcement Learning—0
GHQ: Grouped Hybrid Q Learning for Heterogeneous Cooperative Multi-agent Reinforcement LearningCode0
Compensating for Sensing Failures via Delegation in Human-AI Hybrid Systems—0
Parameter Sharing with Network Pruning for Scalable Multi-Agent Deep Reinforcement Learning—0
Self-Improving Robots: End-to-End Autonomous Visuomotor Reinforcement Learning—0
Reinforcement Learning Guided Multi-Objective Exam Paper GenerationCode0
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Benchmark Results

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