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 12601–12650 of 15113 papers

TitleStatusHype
Don't Forget Your Teacher: A Corrective Reinforcement Learning Framework—0
Effective Medical Test Suggestions Using Deep Reinforcement Learning—0
Combating the Compounding-Error Problem with a Multi-step Model—0
Advantage Amplification in Slowly Evolving Latent-State Environments—0
An Improved Convergence Analysis of Stochastic Variance-Reduced Policy Gradient—0
Linear interpolation gives better gradients than Gaussian smoothing in derivative-free optimization—0
Reinforcement Learning with Policy Mixture Model for Temporal Point Processes Clustering—0
CopyCAT: Taking Control of Neural Policies with Constant Attacks—0
On the Generalization Gap in Reparameterizable Reinforcement Learning—0
Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical Methodology—0
Switching Linear Dynamics for Variational Bayes Filtering—0
Variance Reduction for Evolution Strategies via Structured Control Variates—0
A General Markov Decision Process Framework for Directly Learning Optimal Control Policies—0
Conditions on Features for Temporal Difference-Like Methods to Converge—0
Beyond Exponentially Discounted Sum: Automatic Learning of Return Function—0
Generation of Policy-Level Explanations for Reinforcement Learning—0
Interactive Teaching Algorithms for Inverse Reinforcement Learning—0
Learning robust control for LQR systems with multiplicative noise via policy gradientCode0
Hypothesis-Driven Skill Discovery for Hierarchical Deep Reinforcement Learning—0
Finite-Sample Analysis of Nonlinear Stochastic Approximation with Applications in Reinforcement LearningCode0
Explainable Reinforcement Learning Through a Causal LensCode0
Disentangling Dynamics and Returns: Value Function Decomposition with Future Prediction—0
AgentGraph: Towards Universal Dialogue Management with Structured Deep Reinforcement Learning—0
Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement LearningCode0
Policy Search by Target Distribution Learning for Continuous Control—0
Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy PoliciesCode0
Near-optimal Optimistic Reinforcement Learning using Empirical Bernstein Inequalities—0
Selective Transfer with Reinforced Transfer Network for Partial Domain Adaptation—0
Variational Bayes: A report on approaches and applications—0
Safe Reinforcement Learning with Nonlinear Dynamics via Model Predictive ShieldingCode0
Composing Task-Agnostic Policies with Deep Reinforcement Learning—0
Prioritized Sequence Experience Replay—0
Learning to Reason in Large Theories without Imitation—0
Transferable Cost-Aware Security Policy Implementation for Malware Detection Using Deep Reinforcement Learning—0
A Kernel Loss for Solving the Bellman EquationCode0
Exploration via Flow-Based Intrinsic RewardsCode0
A Dual Reinforcement Learning Framework for Unsupervised Text Style TransferCode0
Adaptive Symmetric Reward Noising for Reinforcement LearningCode0
RL4health: Crowdsourcing Reinforcement Learning for Knee Replacement Pathway Optimization—0
Automatic Machine Learning by Pipeline Synthesis using Model-Based Reinforcement Learning and a Grammar—0
MQLV: Optimal Policy of Money Management in Retail Banking with Q-Learning—0
InfoRL: Interpretable Reinforcement Learning using Information Maximization—0
Continual Reinforcement Learning in 3D Non-stationary EnvironmentsCode0
A Micro-Objective Perspective of Reinforcement Learning—0
Reinforcement Learning in Feature Space: Matrix Bandit, Kernels, and Regret Bound—0
PAC Guarantees for Cooperative Multi-Agent Reinforcement Learning with Restricted Communication—0
Population-based Global Optimisation Methods for Learning Long-term Dependencies with RNNs—0
Multi-hop Reading Comprehension via Deep Reinforcement Learning based Document TraversalCode0
Recurrent Value Functions—0
Hierarchical Reinforcement Learning for Concurrent Discovery of Compound and Composable PoliciesCode0
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Benchmark Results

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