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 9551–9600 of 15113 papers

TitleStatusHype
Ask Your Humans: Using Human Instructions to Improve Generalization in Reinforcement LearningCode1
Game-Theoretic Multiagent Reinforcement LearningCode1
Pseudo Random Number Generation through Reinforcement Learning and Recurrent Neural NetworksCode1
FireCommander: An Interactive, Probabilistic Multi-agent Environment for Heterogeneous Robot TeamsCode1
A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement LearningCode1
POMO: Policy Optimization with Multiple Optima for Reinforcement LearningCode1
Topic-Preserving Synthetic News Generation: An Adversarial Deep Reinforcement Learning Approach—0
Personalized Multimorbidity Management for Patients with Type 2 Diabetes Using Reinforcement Learning of Electronic Health RecordsCode0
Recovery RL: Safe Reinforcement Learning with Learned Recovery ZonesCode1
Few-Shot Complex Knowledge Base Question Answering via Meta Reinforcement LearningCode1
Machine versus Human Attention in Deep Reinforcement Learning Tasks—0
Reinforcement Learning of Causal Variables Using Mediation Analysis—0
How do Offline Measures for Exploration in Reinforcement Learning behave?—0
Learning Personalized Discretionary Lane-Change Initiation for Fully Autonomous Driving Based on Reinforcement Learning—0
Abstract Value Iteration for Hierarchical Reinforcement Learning—0
DeepFoldit -- A Deep Reinforcement Learning Neural Network Folding Proteins—0
Learning to Unknot—0
Learning to Represent Action Values as a Hypergraph on the Action Vertices—0
Designing Interpretable Approximations to Deep Reinforcement Learning—0
Understanding the Pathologies of Approximate Policy Evaluation when Combined with Greedification in Reinforcement Learning—0
Reinforcement Learning for Sparse-Reward Object-Interaction Tasks in a First-person Simulated 3D Environment—0
Multi-Agent Safe Policy Learning for Power Management of Networked Microgrids—0
Learning to be Safe: Deep RL with a Safety Critic—0
Batch Reinforcement Learning with a Nonparametric Off-Policy Policy Gradient—0
Learning Time Reduction Using Warm Start Methods for a Reinforcement Learning Based Supervisory Control in Hybrid Electric Vehicle Applications—0
Behavior Priors for Efficient Reinforcement Learning—0
COG: Connecting New Skills to Past Experience with Offline Reinforcement LearningCode1
Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement LearningCode1
Can Reinforcement Learning for Continuous Control Generalize Across Physics Engines?—0
Learning Financial Asset-Specific Trading Rules via Deep Reinforcement LearningCode1
Affordance as general value function: A computational model—0
RH-Net: Improving Neural Relation Extraction via Reinforcement Learning and Hierarchical Relational SearchingCode0
Conservative Safety Critics for Exploration—0
Succinct and Robust Multi-Agent Communication With Temporal Message ControlCode1
Pairwise heuristic sequence alignment algorithm based on deep reinforcement learning—0
OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning—0
Lyapunov-Based Reinforcement Learning State Estimator—0
Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement LearningCode1
VisualHints: A Visual-Lingual Environment for Multimodal Reinforcement Learning—0
MELD: Meta-Reinforcement Learning from Images via Latent State ModelsCode1
Personalised Meta-path Generation for Heterogeneous GNNsCode1
Track-Assignment Detailed Routing Using Attention-based Policy Model With Supervision—0
Forethought and Hindsight in Credit Assignment—0
High Acceleration Reinforcement Learning for Real-World Juggling with Binary Rewards—0
Contextual Latent-Movements Off-Policy Optimization for Robotic Manipulation Skills—0
Behavioral decision-making for urban autonomous driving in the presence of pedestrians using Deep Recurrent Q-Network—0
Enhancing reinforcement learning by a finite reward response filter with a case study in intelligent structural control—0
How to Make Deep RL Work in PracticeCode0
Adaptive Federated Learning and Digital Twin for Industrial Internet of Things—0
Improving the Exploration of Deep Reinforcement Learning in Continuous Domains using Planning for Policy Search—0
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Benchmark Results

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