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 5251–5275 of 15113 papers

TitleStatusHype
The Ladder in Chaos: A Simple and Effective Improvement to General DRL Algorithms by Policy Path Trimming and Boosting—0
T-Cell Receptor Optimization with Reinforcement Learning and Mutation Policies for Precesion Immunotherapy—0
Reinforced Labels: Multi-Agent Deep Reinforcement Learning for Point-Feature Label Placement—0
Multi-Start Team Orienteering Problem for UAS Mission Re-Planning with Data-Efficient Deep Reinforcement Learning—0
Resource-Constrained Station-Keeping for Helium Balloons using Reinforcement Learning—0
A Variational Approach to Mutual Information-Based Coordination for Multi-Agent Reinforcement Learning—0
A Deep Reinforcement Learning Trader without Offline Training—0
Efficient Exploration Using Extra Safety Budget in Constrained Policy Optimization—0
Graph Reinforcement Learning for Operator Selection in the ALNS Metaheuristic—0
AR3n: A Reinforcement Learning-based Assist-As-Needed Controller for Robotic Rehabilitation—0
Exploiting Multiple Abstractions in Episodic RL via Reward ShapingCode0
Hierarchical Reinforcement Learning in Complex 3D Environments—0
Human-Inspired Framework to Accelerate Reinforcement LearningCode0
Auxiliary Task-based Deep Reinforcement Learning for Quantum Control—0
Learning to Control Autonomous Fleets from Observation via Offline Reinforcement LearningCode0
Minimizing the Outage Probability in a Markov Decision Process—0
Parameter Optimization of LLC-Converter with multiple operation points using Reinforcement Learning—0
Multi-Agent Reinforcement Learning for Pragmatic Communication and Control—0
The Provable Benefits of Unsupervised Data Sharing for Offline Reinforcement Learning—0
Systematic Rectification of Language Models via Dead-end AnalysisCode0
Reinforcement Learning with Depreciating Assets—0
A Reinforcement Learning Approach for Scheduling Problems With Improved Generalization Through Order Swapping—0
Distributional Method for Risk Averse Reinforcement Learning—0
Dynamic Resource Allocation for Metaverse Applications with Deep Reinforcement Learning—0
Exposure-Based Multi-Agent Inspection of a Tumbling Target Using Deep Reinforcement Learning—0
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Benchmark Results

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