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 1301–1325 of 15113 papers

TitleStatusHype
Test Where Decisions Matter: Importance-driven Testing for Deep Reinforcement Learning—0
Overcoming the Curse of Dimensionality in Reinforcement Learning Through Approximate Factorization—0
Reinforcement Learning Framework for Quantitative Trading—0
Doubly Mild Generalization for Offline Reinforcement LearningCode1
QuadWBG: Generalizable Quadrupedal Whole-Body Grasping—0
Reinforcement learning for Quantum Tiq-Taq-ToeCode0
CROPS: A Deployable Crop Management System Over All Possible State Availabilities—0
Fine-Grained Reward Optimization for Machine Translation using Error Severity Mappings—0
Emergent Cooperative Strategies for Multi-Agent Shepherding via Reinforcement Learning—0
Improving Multi-Domain Task-Oriented Dialogue System with Offline Reinforcement Learning—0
Sharp Analysis for KL-Regularized Contextual Bandits and RLHF—0
Q-SFT: Q-Learning for Language Models via Supervised Fine-Tuning—0
Think Smart, Act SMARL! Analyzing Probabilistic Logic Shields for Multi-Agent Reinforcement LearningCode0
Interactive Dialogue Agents via Reinforcement Learning on Hindsight Regenerations—0
A Reinforcement Learning-Based Automatic Video Editing Method Using Pre-trained Vision-Language Model—0
Evaluating Robustness of Reinforcement Learning Algorithms for Autonomous Shipping—0
Enabling Adaptive Agent Training in Open-Ended Simulators by Targeting DiversityCode0
Noisy Zero-Shot Coordination: Breaking The Common Knowledge Assumption In Zero-Shot Coordination GamesCode0
Plasticity Loss in Deep Reinforcement Learning: A Survey—0
Opportunities of Reinforcement Learning in South Africa's Just Transition—0
Approximate Equivariance in Reinforcement Learning—0
A Comparative Study of Deep Reinforcement Learning for Crop Production Management—0
Beyond The Rainbow: High Performance Deep Reinforcement Learning on a Desktop PCCode1
Hybrid Transfer Reinforcement Learning: Provable Sample Efficiency from Shifted-Dynamics DataCode0
Interpretable and Efficient Data-driven Discovery and Control of Distributed Systems—0
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Benchmark Results

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