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 1015110200 of 15113 papers

TitleStatusHype
On the Convergence of Reinforcement Learning with Monte Carlo Exploring Starts0
Soft Expert Reward Learning for Vision-and-Language Navigation0
Multi-agent Reinforcement Learning in Bayesian Stackelberg Markov Games for Adaptive Moving Target Defense0
A Machine Learning Approach for Task and Resource Allocation in Mobile Edge Computing Based Networks0
Lagrangian Duality in Reinforcement Learning0
Active MR k-space Sampling with Reinforcement LearningCode1
Interpretable Control by Reinforcement Learning0
Battlesnake Challenge: A Multi-agent Reinforcement Learning Playground with Human-in-the-loopCode1
A Short Note on Soft-max and Policy Gradients in Bandits Problems0
An Overview of Natural Language State Representation for Reinforcement Learning0
CATCH: Context-based Meta Reinforcement Learning for Transferrable Architecture Search0
Structure Mapping for Transferability of Causal ModelsCode0
Quick Question: Interrupting Users for Microtasks with Reinforcement Learning0
WordCraft: An Environment for Benchmarking Commonsense AgentsCode1
Off-Policy Reinforcement Learning for Efficient and Effective GAN Architecture SearchCode1
Hierarchical Deep Reinforcement Learning Approach for Multi-Objective Scheduling With Varying Queue Sizes0
Hyperparameter Selection for Offline Reinforcement Learning0
Discovering Reinforcement Learning AlgorithmsCode1
Human-like Energy Management Based on Deep Reinforcement Learning and Historical Driving Experiences0
Decision-making Strategy on Highway for Autonomous Vehicles using Deep Reinforcement Learning0
Distributed Reinforcement Learning of Targeted Grasping with Active Vision for Mobile Manipulators0
Dueling Deep Q Network for Highway Decision Making in Autonomous Vehicles: A Case Study0
DRIFT: Deep Reinforcement Learning for Functional Software Testing0
Collision Avoidance Robotics Via Meta-Learning (CARML)Code0
CoNES: Convex Natural Evolutionary Strategies0
Meta-Gradient Reinforcement Learning with an Objective Discovered Online0
Provably Good Batch Reinforcement Learning Without Great ExplorationCode1
Transferred Energy Management Strategies for Hybrid Electric Vehicles Based on Driving Conditions Recognition0
Weighing Counts: Sequential Crowd Counting by Reinforcement LearningCode1
Transfer Deep Reinforcement Learning-enabled Energy Management Strategy for Hybrid Tracked Vehicle0
Reinforcement Learning-Enabled Decision-Making Strategies for a Vehicle-Cyber-Physical-System in Connected Environment0
Information Freshness-Aware Task Offloading in Air-Ground Integrated Edge Computing Systems0
Developmental Reinforcement Learning of Control Policy of a Quadcopter UAV with Thrust Vectoring RotorsCode1
Inverse Reinforcement Learning from a Gradient-based Learner0
Computation Offloading in Beyond 5G Networks: A Distributed Learning Framework and Applications0
Deep PQR: Solving Inverse Reinforcement Learning using Anchor ActionsCode0
Qgraph-bounded Q-learning: Stabilizing Model-Free Off-Policy Deep Reinforcement Learning0
Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity0
Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning0
Learning to Sample with Local and Global Contexts in Experience Replay Buffer0
Single-partition adaptive Q-learningCode0
Learning Robust State Abstractions for Hidden-Parameter Block MDPsCode1
Robustifying Reinforcement Learning Agents via Action Space Adversarial Training0
Revisiting Fundamentals of Experience ReplayCode0
Reinforcement Learning of Musculoskeletal Control from Functional SimulationsCode0
Implicit Distributional Reinforcement LearningCode1
AirCapRL: Autonomous Aerial Human Motion Capture using Deep Reinforcement Learning0
A Provably Efficient Sample Collection Strategy for Reinforcement Learning0
XCS as a reinforcement learning approach to automatic test case prioritizationCode0
Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and Approaches0
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Benchmark Results

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