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 8351–8400 of 15113 papers

TitleStatusHype
Exploratory State Representation LearningCode0
Identifying Reasoning Flaws in Planning-Based RL Using Tree Explanations—0
An Offline Deep Reinforcement Learning for Maintenance Decision-Making—0
Exploring More When It Needs in Deep Reinforcement Learning—0
Deep Reinforcement Learning with Adjustments—0
Efficiently Training On-Policy Actor-Critic Networks in Robotic Deep Reinforcement Learning with Demonstration-like Sampled Exploration—0
DRL-based Slice Placement under Realistic Network Load Conditions—0
From internal models toward metacognitive AI—0
Towards Reinforcement Learning for Pivot-based Neural Machine Translation with Non-autoregressive Transformer—0
Model-Free Reinforcement Learning for Optimal Control of MarkovDecision Processes Under Signal Temporal Logic Specifications—0
MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research—0
On the Feasibility of Learning Finger-gaiting In-hand Manipulation with Intrinsic Sensing—0
Deep Reinforcement Learning for Wireless Scheduling in Distributed Networked Control—0
L^2NAS: Learning to Optimize Neural Architectures via Continuous-Action Reinforcement Learning—0
Adaptive Sampling Quasi-Newton Methods for Zeroth-Order Stochastic Optimization—0
A Graph Policy Network Approach for Volt-Var Control in Power Distribution Systems—0
Go-Blend behavior and affect—0
Learnable Triangulation for Deep Learning-based 3D Reconstruction of Objects of Arbitrary Topology from Single RGB Images—0
Combining Contention-Based Spectrum Access and Adaptive Modulation using Deep Reinforcement Learning—0
The f-Divergence Reinforcement Learning Framework—0
Parameter-free Reduction of the Estimation Bias in Deep Reinforcement Learning for Deterministic Policy GradientsCode0
Regularization Guarantees Generalization in Bayesian Reinforcement Learning through Algorithmic Stability—0
Neuroprospecting with DeepRL agents—0
PredictionNet: Real-Time Joint Probabilistic Traffic Prediction for Planning, Control, and Simulation—0
Reinforcement Learning Under Algorithmic Triage—0
A Multi-Agent Deep Reinforcement Learning Coordination Framework for Connected and Automated Vehicles at Merging Roadways—0
Dimension-Free Rates for Natural Policy Gradient in Multi-Agent Reinforcement Learning—0
Deep Reinforcement Learning-Based Long-Range Autonomous Valet Parking for Smart Cities—0
Hierarchies of Planning and Reinforcement Learning for Robot Navigation—0
Introducing Symmetries to Black Box Meta Reinforcement Learning—0
Benchmarking Lane-changing Decision-making for Deep Reinforcement Learning—0
Adversarial Training Blocks Generalization in Neural Policies—0
Estimation Error Correction in Deep Reinforcement Learning for Deterministic Actor-Critic MethodsCode0
Locality Matters: A Scalable Value Decomposition Approach for Cooperative Multi-Agent Reinforcement Learning—0
A Survey on Reinforcement Learning for Recommender Systems—0
Towards Multi-Agent Reinforcement Learning using Quantum Boltzmann Machines—0
Return Dispersion as an Estimator of Learning Potential for Prioritized Level Replay—0
MEPG: A Minimalist Ensemble Policy Gradient Framework for Deep Reinforcement Learning—0
Example-Driven Model-Based Reinforcement Learning for Solving Long-Horizon Visuomotor Tasks—0
A Distance-based Anomaly Detection Framework for Deep Reinforcement Learning—0
Long-Term Exploration in Persistent MDPsCode0
Learning offline: memory replay in biological and artificial reinforcement learning—0
Generalization in Text-based Games via Hierarchical Reinforcement LearningCode0
ACReL: Adversarial Conditional value-at-risk Reinforcement Learning—0
A Reinforcement Learning Approach to the Stochastic Cutting Stock Problem—0
A Survey of Text Games for Reinforcement Learning informed by Natural Language—0
Learning Natural Language Generation from Scratch—0
Reinforcement Learning for Finite-Horizon Restless Multi-Armed Multi-Action Bandits—0
Two Approaches to Building Collaborative, Task-Oriented Dialog Agents through Self-Play—0
Regularize! Don't Mix: Multi-Agent Reinforcement Learning without Explicit Centralized Structures—0
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Benchmark Results

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