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 12451–12500 of 15113 papers

TitleStatusHype
Finite-time Analysis of Approximate Policy Iteration for the Linear Quadratic Regulator—0
Effective Medical Test Suggestions Using Deep Reinforcement Learning—0
Defining Admissible Rewards for High Confidence Policy Evaluation—0
Towards Finding Longer ProofsCode0
Reinforcement Learning and Adaptive Sampling for Optimized DNN CompilationCode0
Variance Reduction for Evolution Strategies via Structured Control Variates—0
Advantage Amplification in Slowly Evolving Latent-State Environments—0
Linear interpolation gives better gradients than Gaussian smoothing in derivative-free optimization—0
An Improved Convergence Analysis of Stochastic Variance-Reduced Policy Gradient—0
Reinforcement Learning with Policy Mixture Model for Temporal Point Processes Clustering—0
Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical Methodology—0
CopyCAT: Taking Control of Neural Policies with Constant Attacks—0
Switching Linear Dynamics for Variational Bayes Filtering—0
On the Generalization Gap in Reparameterizable Reinforcement Learning—0
Learning robust control for LQR systems with multiplicative noise via policy gradientCode0
Conditions on Features for Temporal Difference-Like Methods to Converge—0
Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement LearningCode1
Beyond Exponentially Discounted Sum: Automatic Learning of Return Function—0
A General Markov Decision Process Framework for Directly Learning Optimal Control Policies—0
Generation of Policy-Level Explanations for Reinforcement Learning—0
Interactive Teaching Algorithms for Inverse Reinforcement Learning—0
Snooping Attacks on Deep Reinforcement LearningCode1
Hypothesis-Driven Skill Discovery for Hierarchical Deep Reinforcement Learning—0
Finite-Sample Analysis of Nonlinear Stochastic Approximation with Applications in Reinforcement LearningCode0
Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy PoliciesCode0
Near-optimal Optimistic Reinforcement Learning using Empirical Bernstein Inequalities—0
Policy Search by Target Distribution Learning for Continuous Control—0
SQIL: Imitation Learning via Reinforcement Learning with Sparse RewardsCode1
Explainable Reinforcement Learning Through a Causal LensCode0
Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement LearningCode0
Disentangling Dynamics and Returns: Value Function Decomposition with Future Prediction—0
AgentGraph: Towards Universal Dialogue Management with Structured Deep Reinforcement Learning—0
Interactive Differentiable SimulationCode2
Selective Transfer with Reinforced Transfer Network for Partial Domain Adaptation—0
Variational Bayes: A report on approaches and applications—0
Prioritized Sequence Experience Replay—0
A Kernel Loss for Solving the Bellman EquationCode0
Transferable Cost-Aware Security Policy Implementation for Malware Detection Using Deep Reinforcement Learning—0
Learning to Reason in Large Theories without Imitation—0
Adversarial Policies: Attacking Deep Reinforcement LearningCode1
Composing Task-Agnostic Policies with Deep Reinforcement Learning—0
Safe Reinforcement Learning with Nonlinear Dynamics via Model Predictive ShieldingCode0
RL4health: Crowdsourcing Reinforcement Learning for Knee Replacement Pathway Optimization—0
MQLV: Optimal Policy of Money Management in Retail Banking with Q-Learning—0
Exploration via Flow-Based Intrinsic RewardsCode0
InfoRL: Interpretable Reinforcement Learning using Information Maximization—0
A Dual Reinforcement Learning Framework for Unsupervised Text Style TransferCode0
A Micro-Objective Perspective of Reinforcement Learning—0
Reinforcement Learning in Feature Space: Matrix Bandit, Kernels, and Regret Bound—0
Adaptive Symmetric Reward Noising for Reinforcement LearningCode0
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Benchmark Results

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