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 10051–10100 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning of Transition States—0
Robust Quadruped Jumping via Deep Reinforcement Learning—0
Scaffolding Reflection in Reinforcement Learning Framework for Confinement Escape Problem—0
Reinforcement Learning Control of a Biomechanical Model of the Upper Extremity—0
Phoebe: Reuse-Aware Online Caching with Reinforcement Learning for Emerging Storage Models—0
Reinforcement Learning Control of Constrained Dynamic Systems with Uniformly Ultimate Boundedness Stability Guarantee—0
Robotic self-representation improves manipulation skills and transfer learning—0
Query-based Targeted Action-Space Adversarial Policies on Deep Reinforcement Learning AgentsCode0
Self-supervised reinforcement learning for speaker localisation with the iCub humanoid robot—0
Steady State Analysis of Episodic Reinforcement Learning—0
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges—0
Imposing Robust Structured Control Constraint on Reinforcement Learning of Linear Quadratic Regulator—0
Hierarchical reinforcement learning for efficient exploration and transfer—0
Griddly: A platform for AI research in games—0
Adaptive Neural Architectures for Recommender Systems—0
On Using Hamiltonian Monte Carlo Sampling for Reinforcement Learning Problems in High-dimension—0
CRPO: A New Approach for Safe Reinforcement Learning with Convergence Guarantee—0
Behaviorally Diverse Traffic Simulation via Reinforcement Learning—0
Proximal Policy Optimization via Enhanced Exploration Efficiency—0
Reinforcement Learning with Dual-Observation for General Video Game PlayingCode0
Non-local Optimization: Imposing Structure on Optimization Problems by Relaxation—0
Reinforcement Learning with Time-dependent Goals for Robotic Musicians—0
Reinforcement Learning Experiments and Benchmark for Solving Robotic Reaching TasksCode0
Offline Learning of Counterfactual Predictions for Real-World Robotic Reinforcement Learning—0
Sample Complexity Bounds for Two Timescale Value-based Reinforcement Learning Algorithms—0
Model-based Reinforcement Learning from Signal Temporal Logic Specifications—0
Perturbation-based exploration methods in deep reinforcement learning—0
What Did You Think Would Happen? Explaining Agent Behaviour Through Intended OutcomesCode0
Hierarchical Reinforcement Learning for Relay Selection and Power Optimization in Two-Hop Cooperative Relay Network—0
Dirichlet policies for reinforced factor portfolios—0
Kinematics-Guided Reinforcement Learning for Object-Aware 3D Ego-Pose Estimation—0
Deep reinforcement learning for RAN optimization and control—0
Automated Adversary Emulation for Cyber-Physical Systems via Reinforcement Learning—0
Learning to Compose Hierarchical Object-Centric Controllers for Robotic Manipulation—0
Deep Reinforcement Learning for Navigation in AAA Video Games—0
Challenges of Applying Deep Reinforcement Learning in Dynamic Dispatching—0
Combining Propositional Logic Based Decision Diagrams with Decision Making in Urban Systems—0
Behavior Planning at Urban Intersections through Hierarchical Reinforcement Learning—0
On Function Approximation in Reinforcement Learning: Optimism in the Face of Large State Spaces—0
Reinforcement Learning for Autonomous Driving with Latent State Inference and Spatial-Temporal Relationships—0
Safe Trajectory Planning Using Reinforcement Learning for Self Driving—0
Multi-Agent Reinforcement Learning for Channel Assignment and Power Allocation in Platoon-Based C-V2X Systems—0
Optimizing Age of Information Through Aerial Reconfigurable Intelligent Surfaces: A Deep Reinforcement Learning Approach—0
On the role of planning in model-based deep reinforcement learning—0
Reliable Off-policy Evaluation for Reinforcement Learning—0
Reinforcement Learning for Assignment problem—0
Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient—0
Online Sparse Reinforcement Learning—0
Exploring market power using deep reinforcement learning for intelligent bidding strategies—0
Universal Activation Function For Machine Learning—0
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Benchmark Results

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