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 3676–3700 of 15113 papers

TitleStatusHype
A Deep Reinforcement Learning Trader without Offline Training—0
A Variational Approach to Mutual Information-Based Coordination for Multi-Agent Reinforcement Learning—0
LS-IQ: Implicit Reward Regularization for Inverse Reinforcement LearningCode1
Human-Inspired Framework to Accelerate Reinforcement LearningCode0
Parameter Optimization of LLC-Converter with multiple operation points using Reinforcement Learning—0
Learning to Control Autonomous Fleets from Observation via Offline Reinforcement LearningCode0
Efficient Exploration Using Extra Safety Budget in Constrained Policy Optimization—0
Auxiliary Task-based Deep Reinforcement Learning for Quantum Control—0
Multi-Agent Reinforcement Learning for Pragmatic Communication and Control—0
Graph Reinforcement Learning for Operator Selection in the ALNS Metaheuristic—0
Minimizing the Outage Probability in a Markov Decision Process—0
Exploiting Multiple Abstractions in Episodic RL via Reward ShapingCode0
AR3n: A Reinforcement Learning-based Assist-As-Needed Controller for Robotic Rehabilitation—0
Hierarchical Reinforcement Learning in Complex 3D Environments—0
The In-Sample Softmax for Offline Reinforcement LearningCode1
Distributional Method for Risk Averse Reinforcement Learning—0
Exposure-Based Multi-Agent Inspection of a Tumbling Target Using Deep Reinforcement Learning—0
Reward Design with Language ModelsCode2
Reinforcement Learning with Depreciating Assets—0
The Provable Benefits of Unsupervised Data Sharing for Offline Reinforcement Learning—0
A Reinforcement Learning Approach for Scheduling Problems With Improved Generalization Through Order Swapping—0
Systematic Rectification of Language Models via Dead-end AnalysisCode0
Dynamic Resource Allocation for Metaverse Applications with Deep Reinforcement Learning—0
Sim-and-Real Reinforcement Learning for Manipulation: A Consensus-based Approach—0
Revolutionizing Genomics with Reinforcement Learning Techniques—0
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Benchmark Results

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