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 8701–8725 of 15113 papers

TitleStatusHype
The Logical Options Framework—0
Memory-based Deep Reinforcement Learning for POMDPsCode1
A Robotic Model of Hippocampal Reverse Replay for Reinforcement Learning—0
Honey, I Shrunk The Actor: A Case Study on Preserving Performance with Smaller Actors in Actor-Critic RL—0
State Augmented Constrained Reinforcement Learning: Overcoming the Limitations of Learning with Rewards—0
MUSBO: Model-based Uncertainty Regularized and Sample Efficient Batch Optimization for Deployment Constrained Reinforcement Learning—0
Mixed Policy Gradient: off-policy reinforcement learning driven jointly by data and modelCode1
School of hard knocks: Curriculum analysis for Pommerman with a fixed computational budget—0
Greedy-Step Off-Policy Reinforcement Learning—0
DeepThermal: Combustion Optimization for Thermal Power Generating Units Using Offline Reinforcement Learning—0
Differentiable Logic Machines—0
Efficient Text-based Reinforcement Learning by Jointly Leveraging State and Commonsense Graph Representations—0
Action Redundancy in Reinforcement Learning—0
A Novel Framework for Neural Architecture Search in the Hill Climbing Domain—0
Uncertainty Estimation Using Riemannian Model~Dynamics for Offline Reinforcement Learning—0
Exploring Supervised and Unsupervised Rewards in Machine TranslationCode1
Explore the Context: Optimal Data Collection for Context-Conditional Dynamics ModelsCode0
Exploiting Multimodal Reinforcement Learning for Simultaneous Machine TranslationCode1
Stratified Experience Replay: Correcting Multiplicity Bias in Off-Policy Reinforcement Learning—0
SENTINEL: Taming Uncertainty with Ensemble-based Distributional Reinforcement Learning—0
Reinforcement Learning with Prototypical RepresentationsCode1
Program Synthesis Guided Reinforcement Learning for Partially Observed EnvironmentsCode1
Reinforcement Learning of the Prediction Horizon in Model Predictive Control—0
Return-Based Contrastive Representation Learning for Reinforcement Learning—0
Provably Improved Context-Based Offline Meta-RL with Attention and Contrastive Learning—0
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Benchmark Results

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