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 9176–9200 of 15113 papers

TitleStatusHype
CityLearn: Standardizing Research in Multi-Agent Reinforcement Learning for Demand Response and Urban Energy ManagementCode1
Hierarchical principles of embodied reinforcement learning: A review—0
Exact Reduction of Huge Action Spaces in General Reinforcement Learning—0
Content Masked Loss: Human-Like Brush Stroke Planning in a Reinforcement Learning Painting AgentCode1
Exploring Fluent Query Reformulations with Text-to-Text Transformers and Reinforcement Learning—0
Reinforcement Learning for Test Case Prioritization—0
Improving the Efficient Neural Architecture Search via Rewarding ModificationsCode0
Curiosity in exploring chemical space: Intrinsic rewards for deep molecular reinforcement learning—0
High-Throughput Synchronous Deep RLCode1
Interactive Question Clarification in Dialogue via Reinforcement Learning—0
Learning Fair Policies in Decentralized Cooperative Multi-Agent Reinforcement LearningCode1
Autotelic Agents with Intrinsically Motivated Goal-Conditioned Reinforcement Learning: a Short Survey—0
MAGNet: Multi-agent Graph Network for Deep Multi-agent Reinforcement Learning—0
ViNG: Learning Open-World Navigation with Visual Goals—0
Model-free and Bayesian Ensembling Model-based Deep Reinforcement Learning for Particle Accelerator Control Demonstrated on the FERMI FELCode0
Towards Optimal District Heating Temperature Control in China with Deep Reinforcement Learning—0
CARLA Real Traffic Scenarios -- novel training ground and benchmark for autonomous driving—0
Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph MatchingCode2
Learning to Run with Potential-Based Reward Shaping and Demonstrations from Video Data—0
A comparative evaluation of machine learning methods for robot navigation through human crowds—0
Batch-Constrained Distributional Reinforcement Learning for Session-based Recommendation—0
Learning Accurate Long-term Dynamics for Model-based Reinforcement LearningCode2
Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation—0
Grounding Artificial Intelligence in the Origins of Human Behavior—0
Gegelati: Lightweight Artificial Intelligence through Generic and Evolvable Tangled Program Graphs—0
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Benchmark Results

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