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

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Showing 9101–9150 of 15113 papers

TitleStatusHype
What is Going on Inside Recurrent Meta Reinforcement Learning Agents?—0
Pre-training of Deep RL Agents for Improved Learning under Domain Randomization—0
Adversarial Inverse Reinforcement Learning for Mean Field Games—0
Using Meta Reinforcement Learning to Bridge the Gap between Simulation and Experiment in Energy Demand Response—0
Medium Access using Distributed Reinforcement Learning for IoTs with Low-Complexity Wireless Transceivers—0
Adapting to Reward Progressivity via Spectral Reinforcement LearningCode0
Emotional Contagion-Aware Deep Reinforcement Learning for Antagonistic Crowd Simulation—0
Hypernetwork Dismantling via Deep Reinforcement Learning—0
End-to-End Intersection Handling using Multi-Agent Deep Reinforcement Learning—0
A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learning—0
A Reinforcement Learning Environment for Polyhedral Optimizations—0
Reward (Mis)design for Autonomous Driving—0
Semi-On-Policy Training for Sample Efficient Multi-Agent Policy Gradients—0
Implementing Reinforcement Learning Algorithms in Retail Supply Chains with OpenAI Gym Toolkit—0
Controlling earthquake-like instabilities using artificial intelligence—0
Adaptive Adversarial Training for Meta Reinforcement Learning—0
ANT: Learning Accurate Network Throughput for Better Adaptive Video Streaming—0
End-to-end grasping policies for human-in-the-loop robots via deep reinforcement learningCode0
A Deep Reinforcement Learning Approach for the Meal Delivery Problem—0
DisCo RL: Distribution-Conditioned Reinforcement Learning for General-Purpose Policies—0
Safe Chance Constrained Reinforcement Learning for Batch Process ControlCode0
Reinforcement Learning using Guided Observability—0
Reset-Free Reinforcement Learning via Multi-Task Learning: Learning Dexterous Manipulation Behaviors without Human Intervention—0
Formula RL: Deep Reinforcement Learning for Autonomous Racing using Telemetry Data—0
A learning gap between neuroscience and reinforcement learningCode0
CVLight: Decentralized Learning for Adaptive Traffic Signal Control with Connected Vehicles—0
Reinforcement Learning for Traffic Signal Control: Comparison with Commercial Systems—0
Tackling Variabilities in Autonomous Driving—0
Policy Fusion for Adaptive and Customizable Reinforcement Learning Agents—0
Model-aided Deep Reinforcement Learning for Sample-efficient UAV Trajectory Design in IoT Networks—0
Model-predictive control and reinforcement learning in multi-energy system case studies—0
Network Defense is Not a Game—0
Outcome-Driven Reinforcement Learning via Variational Inference—0
Scalable Synthesis of Verified Controllers in Deep Reinforcement Learning—0
Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning—0
Prospective Artificial Intelligence Approaches for Active Cyber Defence—0
DRL: Deep Reinforcement Learning for Intelligent Robot Control -- Concept, Literature, and Future—0
Discovering an Aid Policy to Minimize Student Evasion Using Offline Reinforcement Learning—0
GLiDE: Generalizable Quadrupedal Locomotion in Diverse Environments with a Centroidal Model—0
Agent-Centric Representations for Multi-Agent Reinforcement Learning—0
Constraints Satisfiability Driven Reinforcement Learning for Autonomous Cyber Defense—0
Approximated Multi-Agent Fitted Q Iteration—0
Adaptive learning for financial markets mixing model-based and model-free RL for volatility targeting—0
Deep Reinforcement Learning in a Monetary Model—0
Training Value-Aligned Reinforcement Learning Agents Using a Normative Prior—0
Probabilistic Mixture-of-Experts for Efficient Deep Reinforcement LearningCode0
Singular Perturbation-based Reinforcement Learning of Two-Point Boundary Optimal Control Systems—0
Reinforcement learning for linear-convex models with jumps via stability analysis of feedback controls—0
Quick Learner Automated Vehicle Adapting its Roadmanship to Varying Traffic Cultures with Meta Reinforcement Learning—0
Reinforcement learning based process optimization and strategy development in conventional tunnelingCode0
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Benchmark Results

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