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

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Showing 11101–11150 of 15113 papers

TitleStatusHype
Extended Markov Games to Learn Multiple Tasks in Multi-Agent Reinforcement LearningCode0
An Inductive Bias for Distances: Neural Nets that Respect the Triangle InequalityCode1
Resource Management in Wireless Networks via Multi-Agent Deep Reinforcement Learning—0
Robust Reinforcement Learning via Adversarial training with Langevin DynamicsCode0
Hoplite: Efficient and Fault-Tolerant Collective Communication for Task-Based Distributed SystemsCode1
Deep Reinforcement Learning-Based Beam Tracking for Low-Latency Services in Vehicular Networks—0
Fast Reinforcement Learning for Anti-jamming Communications—0
Improving Generalization of Reinforcement Learning with Minimax Distributional Soft Actor-Critic—0
Effective Reinforcement Learning through Evolutionary Surrogate-Assisted PrescriptionCode1
MODRL/D-AM: Multiobjective Deep Reinforcement Learning Algorithm Using Decomposition and Attention Model for Multiobjective Optimization—0
Multi-Vehicle Routing Problems with Soft Time Windows: A Multi-Agent Reinforcement Learning Approach—0
A Tensor Network Approach to Finite Markov Decision Processes—0
Data Efficient Training for Reinforcement Learning with Adaptive Behavior Policy Sharing—0
Regret Bounds for Discounted MDPs—0
On the Convergence Theory of Debiased Model-Agnostic Meta-Reinforcement LearningCode0
Towards Intelligent Pick and Place Assembly of Individualized Products Using Reinforcement Learning—0
Objective Mismatch in Model-based Reinforcement LearningCode1
Machine Learning Approaches For Motor Learning: A Short Review—0
Reinforcement Learning Enhanced Quantum-inspired Algorithm for Combinatorial OptimizationCode1
Learning to Switch Among Agents in a Team via 2-Layer Markov Decision Processes—0
HMRL: Hyper-Meta Learning for Sparse Reward Reinforcement Learning Problem—0
Learning Structured Communication for Multi-agent Reinforcement Learning—0
Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning—0
On Reward Shaping for Mobile Robot Navigation: A Reinforcement Learning and SLAM Based Approach—0
Proficiency Constrained Multi-Agent Reinforcement Learning for Environment-Adaptive Multi UAV-UGV Teaming—0
Provable Self-Play Algorithms for Competitive Reinforcement Learning—0
On the Convergence of the Monte Carlo Exploring Starts Algorithm for Reinforcement Learning—0
SparseIDS: Learning Packet Sampling with Reinforcement LearningCode1
Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions—0
Discrete Action On-Policy Learning with Action-Value CriticCode0
Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction StatesCode1
Reward Tweaking: Maximizing the Total Reward While Planning for Short Horizons—0
A Deep Reinforcement Learning Algorithm Using Dynamic Attention Model for Vehicle Routing ProblemsCode1
Analyzing Policy Distillation on Multi-Task Learning and Meta-Reinforcement Learning in Meta-World—0
Learning State Abstractions for Transfer in Continuous ControlCode0
BRPO: Batch Residual Policy Optimization—0
Conservative Exploration in Reinforcement Learning—0
A data-driven choice of misfit function for FWI using reinforcement learning—0
Inferential Induction: A Novel Framework for Bayesian Reinforcement Learning—0
Description Based Text Classification with Reinforcement Learning—0
RL-Duet: Online Music Accompaniment Generation Using Deep Reinforcement Learning—0
Multi-task Reinforcement Learning with a Planning Quasi-Metric—0
Manipulating Reinforcement Learning: Poisoning Attacks on Cost Signals—0
Bayesian Residual Policy Optimization: Scalable Bayesian Reinforcement Learning with Clairvoyant Experts—0
Explicit Mean-Square Error Bounds for Monte-Carlo and Linear Stochastic Approximation—0
Causally Correct Partial Models for Reinforcement Learning—0
Accelerating Reinforcement Learning for Reaching using Continuous Curriculum Learning—0
Automated Lane Change Strategy using Proximal Policy Optimization-based Deep Reinforcement Learning—0
Reward-Free Exploration for Reinforcement Learning—0
Student/Teacher Advising through Reward Augmentation—0
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Benchmark Results

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