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

Showing 75517600 of 15113 papers

TitleStatusHype
Identifying optimal cycles in quantum thermal machines with reinforcement-learningCode0
Learning Meta Representations for Agents in Multi-Agent Reinforcement Learning0
Integrated Decision and Control at Multi-Lane Intersections with Mixed Traffic Flow0
SurRoL: An Open-source Reinforcement Learning Centered and dVRK Compatible Platform for Surgical Robot LearningCode1
Reinforcement Learning Based Sparse Black-box Adversarial Attack on Video Recognition Models0
A Policy Efficient Reduction Approach to Convex Constrained Deep Reinforcement Learning0
Influence-Based Reinforcement Learning for Intrinsically-Motivated Agents0
Active Inference for Stochastic ControlCode1
Deep Reinforcement Learning for Wireless Resource Allocation Using Buffer State Information0
Reinforcement Learning based Condition-oriented Maintenance Scheduling for Flow Line SystemsCode1
ReGen: Reinforcement Learning for Text and Knowledge Base Generation using Pretrained Language ModelsCode1
Reinforcement Learning-powered Semantic Communication via Semantic SimilarityCode1
WAD: A Deep Reinforcement Learning Agent for Urban Autonomous Driving0
Deep Reinforcement Learning for Dynamic Band Switch in Cellular-Connected UAV0
Federated Reinforcement Learning: Techniques, Applications, and Open Challenges0
Adaptive Control of Differentially Private Linear Quadratic Systems0
Model-based Chance-Constrained Reinforcement Learning via Separated Proportional-Integral Lagrangian0
Robust Model-based Reinforcement Learning for Autonomous Greenhouse Control0
When should agents explore?0
Responsive Regulation of Dynamic UAV Communication Networks Based on Deep Reinforcement LearningCode1
Adversary agent reinforcement learning for pursuit-evasion0
Deep Reinforcement Learning in Computer Vision: A Comprehensive Survey0
Self-optimizing adaptive optics control with Reinforcement Learning for high-contrast imaging0
Entropy-Aware Model Initialization for Effective Exploration in Deep Reinforcement Learning0
Robust Risk-Aware Reinforcement LearningCode1
Power Grid Cascading Failure Mitigation by Reinforcement Learning0
No DBA? No regret! Multi-armed bandits for index tuning of analytical and HTAP workloads with provable guarantees0
Collect & Infer -- a fresh look at data-efficient Reinforcement Learning0
A Boosting Approach to Reinforcement Learning0
MimicBot: Combining Imitation and Reinforcement Learning to win in Bot Bowl0
An Independent Study of Reinforcement Learning and Autonomous Driving0
Explainable Reinforcement Learning for Broad-XAI: A Conceptual Framework and Survey0
Crown Jewels Analysis using Reinforcement Learning with Attack Graphs0
Reinforcement Learning to Optimize Lifetime Value in Cold-Start Recommendation0
Plug and Play, Model-Based Reinforcement Learning0
Cooperative Localization Utilizing Reinforcement Learning for 5G Networks0
Global Convergence of the ODE Limit for Online Actor-Critic Algorithms in Reinforcement Learning0
A Reinforcement Learning Approach for GNSS Spoofing Attack Detection of Autonomous Vehicles0
Settling the Variance of Multi-Agent Policy GradientsCode1
Trends in Neural Architecture Search: Towards the Acceleration of Search0
Provable Benefits of Actor-Critic Methods for Offline Reinforcement Learning0
Explainable Deep Reinforcement Learning Using Introspection in a Non-episodic Task0
End-to-End Urban Driving by Imitating a Reinforcement Learning CoachCode1
Reinforce Attack: Adversarial Attack against BERT with Reinforcement Learning0
Optimal Placement of Public Electric Vehicle Charging Stations Using Deep Reinforcement Learning0
Revisiting State Augmentation methods for Reinforcement Learning with Stochastic DelaysCode0
Monolithic vs. hybrid controller for multi-objective Sim-to-Real learningCode0
The Ecosystem Path to General AI0
Heterotic String Model Building with Monad Bundles and Reinforcement Learning0
Introduction to Quantum Reinforcement Learning: Theory and PennyLane-based Implementation0
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Benchmark Results

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