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 10151–10175 of 15113 papers

TitleStatusHype
On the Convergence of Reinforcement Learning with Monte Carlo Exploring Starts—0
Soft Expert Reward Learning for Vision-and-Language Navigation—0
Multi-agent Reinforcement Learning in Bayesian Stackelberg Markov Games for Adaptive Moving Target Defense—0
A Machine Learning Approach for Task and Resource Allocation in Mobile Edge Computing Based Networks—0
Lagrangian Duality in Reinforcement Learning—0
Active MR k-space Sampling with Reinforcement LearningCode1
Interpretable Control by Reinforcement Learning—0
Battlesnake Challenge: A Multi-agent Reinforcement Learning Playground with Human-in-the-loopCode1
A Short Note on Soft-max and Policy Gradients in Bandits Problems—0
An Overview of Natural Language State Representation for Reinforcement Learning—0
CATCH: Context-based Meta Reinforcement Learning for Transferrable Architecture Search—0
Structure Mapping for Transferability of Causal ModelsCode0
Quick Question: Interrupting Users for Microtasks with Reinforcement Learning—0
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 Sizes—0
Hyperparameter Selection for Offline Reinforcement Learning—0
Discovering Reinforcement Learning AlgorithmsCode1
Human-like Energy Management Based on Deep Reinforcement Learning and Historical Driving Experiences—0
Decision-making Strategy on Highway for Autonomous Vehicles using Deep Reinforcement Learning—0
Distributed Reinforcement Learning of Targeted Grasping with Active Vision for Mobile Manipulators—0
Dueling Deep Q Network for Highway Decision Making in Autonomous Vehicles: A Case Study—0
DRIFT: Deep Reinforcement Learning for Functional Software Testing—0
Collision Avoidance Robotics Via Meta-Learning (CARML)Code0
CoNES: Convex Natural Evolutionary Strategies—0
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Benchmark Results

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