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 56015650 of 15113 papers

TitleStatusHype
Off-Policy Reinforcement Learning with Loss Function Weighted by Temporal Difference Error0
Novel Reinforcement Learning Algorithm for Suppressing Synchronization in Closed Loop Deep Brain Stimulators0
Understanding the Complexity Gains of Single-Task RL with a Curriculum0
Streaming Traffic Flow Prediction Based on Continuous Reinforcement Learning0
SHIRO: Soft Hierarchical Reinforcement Learning0
Automated Gadget Discovery in ScienceCode0
Deep Reinforcement Learning for Heat Pump Control0
Investigation of reinforcement learning for shape optimization of profile extrusion dies0
NARS vs. Reinforcement learning: ONA vs. Q-LearningCode0
Offline Reinforcement Learning for Human-Guided Human-Machine Interaction with Private Information0
Reinforcement Learning Based Approaches to Adaptive Context Caching in Distributed Context Management Systems0
A Learned Simulation Environment to Model Student Engagement and Retention in Automated Online Courses0
Certified Policy Smoothing for Cooperative Multi-Agent Reinforcement LearningCode0
Decoding surface codes with deep reinforcement learning and probabilistic policy reuse0
Hyperparameters in Contextual RL are Highly SituationalCode0
Imitation Is Not Enough: Robustifying Imitation with Reinforcement Learning for Challenging Driving Scenarios0
Lifelong Reinforcement Learning with Modulating MasksCode0
A Memetic Algorithm with Reinforcement Learning for Sociotechnical Production Scheduling0
Control of Continuous Quantum Systems with Many Degrees of Freedom based on Convergent Reinforcement LearningCode0
Robust Path Selection in Software-defined WANs using Deep Reinforcement Learning0
Neighboring state-based RL Exploration0
Variational Quantum Soft Actor-Critic for Robotic Arm Control0
AdverSAR: Adversarial Search and Rescue via Multi-Agent Reinforcement Learning0
Bandit approach to conflict-free multi-agent Q-learning in view of photonic implementation0
I Cast Detect Thoughts: Learning to Converse and Guide with Intents and Theory-of-Mind in Dungeons and Dragons0
Adapting the Exploration Rate for Value-of-Information-Based Reinforcement Learning0
Inverse Reinforcement Learning for Text Summarization0
Dexterous Manipulation from Images: Autonomous Real-World RL via Substep Guidance0
Near-optimal Policy Identification in Active Reinforcement Learning0
Taming Lagrangian Chaos with Multi-Objective Reinforcement Learning0
Quantum policy gradient algorithms0
Risk-Sensitive Reinforcement Learning with Exponential Criteria0
Neural Coreference Resolution based on Reinforcement Learning0
Managing Temporal Resolution in Continuous Value Estimation: A Fundamental Trade-off0
Pre-Trained Image Encoder for Generalizable Visual Reinforcement Learning0
Enhancing Cyber Resilience of Networked Microgrids using Vertical Federated Reinforcement Learning0
Cognitive Level-k Meta-Learning for Safe and Pedestrian-Aware Autonomous Driving0
Latent Variable Representation for Reinforcement Learning0
Comparison of Model-Free and Model-Based Learning-Informed Planning for PointGoal NavigationCode0
Offline Robot Reinforcement Learning with Uncertainty-Guided Human Expert Sampling0
Safe Evaluation For Offline Learning: Are We Ready To Deploy?0
An Energy-aware and Fault-tolerant Deep Reinforcement Learning based approach for Multi-agent Patrolling Problems0
Reinforcement Learning for Agile Active Target Sensing with a UAV0
Reinforcement Learning in Credit Scoring and Underwriting0
Residual Policy Learning for Powertrain Control0
Multi-Agent Reinforcement Learning with Shared Resources for Inventory Management0
Towards Hardware-Specific Automatic Compression of Neural Networks0
Active Inference and Reinforcement Learning: A unified inference on continuous state and action spaces under partial observability0
Distributed-Training-and-Execution Multi-Agent Reinforcement Learning for Power Control in HetNetCode0
Emergent Behaviors in Multi-Agent Target Acquisition0
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Benchmark Results

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