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 5926–5950 of 15113 papers

TitleStatusHype
Goal Exploration Augmentation via Pre-trained Skills for Sparse-Reward Long-Horizon Goal-Conditioned Reinforcement LearningCode0
Reinforcement Learning-based Defect Mitigation for Quality Assurance of Additive Manufacturing—0
Nonuniqueness and Convergence to Equivalent Solutions in Observer-based Inverse Reinforcement Learning—0
Using Contrastive Samples for Identifying and Leveraging Possible Causal Relationships in Reinforcement Learning—0
SAM-RL: Sensing-Aware Model-Based Reinforcement Learning via Differentiable Physics-Based Simulation and Rendering—0
Towards customizable reinforcement learning agents: Enabling preference specification through online vocabulary expansion—0
Many-Objective Reinforcement Learning for Online Testing of DNN-Enabled Systems—0
Meta-Reinforcement Learning Using Model Parameters—0
Hybrid Indoor Localization via Reinforcement Learning-based Information Fusion—0
A Bibliometric Analysis and Review on Reinforcement Learning for Transportation Applications—0
D-Shape: Demonstration-Shaped Reinforcement Learning via Goal Conditioning—0
Knowledge-Guided Exploration in Deep Reinforcement Learning—0
Environment Design for Inverse Reinforcement LearningCode0
Uncertainty-based Meta-Reinforcement Learning for Robust Radar Tracking—0
Quantum deep recurrent reinforcement learning—0
Shortest Edit Path Crossover: A Theory-driven Solution to the Permutation Problem in Evolutionary Neural Architecture SearchCode0
One-shot, Offline and Production-Scalable PID Optimisation with Deep Reinforcement Learning—0
Bridging Distributional and Risk-sensitive Reinforcement Learning with Provable Regret Bounds—0
Entity Divider with Language Grounding in Multi-Agent Reinforcement Learning—0
AACHER: Assorted Actor-Critic Deep Reinforcement Learning with Hindsight Experience ReplayCode0
Graph Reinforcement Learning-based CNN Inference Offloading in Dynamic Edge Computing—0
Classifying Ambiguous Identities in Hidden-Role Stochastic Games with Multi-Agent Reinforcement LearningCode0
Dichotomy of Control: Separating What You Can Control from What You Cannot—0
Causal Explanation for Reinforcement Learning: Quantifying State and Temporal Importance—0
Hardness in Markov Decision Processes: Theory and Practice—0
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Benchmark Results

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