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 11901–11950 of 15113 papers

TitleStatusHype
Single Episode Policy Transfer in Reinforcement LearningCode0
Adaptive Discretization for Episodic Reinforcement Learning in Metric SpacesCode0
Adaptive Curriculum Generation from Demonstrations for Sim-to-Real Visuomotor ControlCode0
Adaptive Trade-Offs in Off-Policy Learning—0
Conditional Importance Sampling for Off-Policy Learning—0
Deep Reinforcement Learning meets Graph Neural Networks: exploring a routing optimization use caseCode0
Creativity in Robot Manipulation with Deep Reinforcement Learning—0
Soft Actor-Critic for Discrete Action SettingsCode0
Parallel Exploration via Negatively Correlated Search—0
Reinforcement Learning for Robotic Manipulation using Simulated Locomotion DemonstrationsCode0
Reinforced Bit Allocation under Task-Driven Semantic Distortion Metrics—0
Model-free Reinforcement Learning in Infinite-horizon Average-reward Markov Decision ProcessesCode0
On the Reduction of Variance and Overestimation of Deep Q-Learning—0
Actor Critic with Differentially Private Critic—0
A unified view of likelihood ratio and reparameterization gradients and an optimal importance sampling scheme—0
Coordination of PV Smart Inverters Using Deep Reinforcement Learning for Grid Voltage Regulation—0
On the Expressivity of Neural Networks for Deep Reinforcement LearningCode0
Dynamic Graph Configuration with Reinforcement Learning for Connected Autonomous Vehicle Trajectories—0
Federated Transfer Reinforcement Learning for Autonomous Driving—0
Policy Poisoning in Batch Reinforcement Learning and ControlCode0
Neural Program Synthesis By Self-Learning—0
Rethinking Exposure Bias In Language Modeling—0
QoS and Jamming-Aware Wireless Networking Using Deep Reinforcement Learning—0
Uncertainty Quantification and Exploration for Reinforcement Learning—0
Influence-Based Multi-Agent ExplorationCode0
Curiosity-Driven Recommendation Strategy for Adaptive Learning via Deep Reinforcement Learning—0
Autonomous Navigation via Deep Reinforcement Learning for Resource Constraint Edge Nodes using Transfer LearningCode0
Building HVAC Scheduling Using Reinforcement Learning via Neural Network Based Model Approximation—0
Network Randomization: A Simple Technique for Generalization in Deep Reinforcement LearningCode0
Improving Gradient Estimation in Evolutionary Strategies With Past Descent Directions—0
Green Deep Reinforcement Learning for Radio Resource Management: Architecture, Algorithm Compression and Challenge—0
Modeling Cyber-Physical Human Systems via an Interplay Between Reinforcement Learning and Game Theory—0
RLCard: A Toolkit for Reinforcement Learning in Card GamesCode0
Hierarchical Reinforcement Learning with Advantage-Based Auxiliary RewardsCode0
Autonomous Driving using Safe Reinforcement Learning by Incorporating a Regret-based Human Lane-Changing Decision Model—0
Fast Task-Adaptation for Tasks Labeled Using Natural Language in Reinforcement Learning—0
Linear-Quadratic Mean-Field Reinforcement Learning: Convergence of Policy Gradient Methods—0
Defensive Escort Teams via Multi-Agent Deep Reinforcement Learning—0
Improving Generalization in Meta Reinforcement Learning using Learned Objectives—0
Integrating Behavior Cloning and Reinforcement Learning for Improved Performance in Dense and Sparse Reward Environments—0
Imagined Value Gradients: Model-Based Policy Optimization with Transferable Latent Dynamics Models—0
Ctrl-Z: Recovering from Instability in Reinforcement Learning—0
A Dual-Hormone Closed-Loop Delivery System for Type 1 Diabetes Using Deep Reinforcement Learning—0
Black-box Optimizer with Implicit Natural Gradient—0
Multiple-objective Reinforcement Learning for Inverse Design and Identification—0
Model-based Reinforcement Learning for Predictions and Control for Limit Order Books—0
Model-Based Reinforcement Learning Exploiting State-Action Equivalence—0
TorchBeast: A PyTorch Platform for Distributed RLCode0
Tactical Reward Shaping: Bypassing Reinforcement Learning with Strategy-Based Goals—0
Reinforcement Learning with Structured Hierarchical Grammar Representations of Actions—0
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Benchmark Results

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