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 9951–10000 of 15113 papers

TitleStatusHype
Non-Crossing Quantile Regression for Distributional Reinforcement Learning—0
Provably Good Batch Off-Policy Reinforcement Learning Without Great Exploration—0
MOReL: Model-Based Offline Reinforcement Learning—0
On the Stability and Convergence of Robust Adversarial Reinforcement Learning: A Case Study on Linear Quadratic Systems—0
On the Convergence of Smooth Regularized Approximate Value Iteration Schemes—0
Reinforcement Learning for Control with Multiple Frequencies—0
Provably Efficient Reinforcement Learning with Kernel and Neural Function Approximations—0
Obtain Employee Turnover Rate and Optimal Reduction Strategy Based On Neural Network and Reinforcement Learning—0
Storage Efficient and Dynamic Flexible Runtime Channel Pruning via Deep Reinforcement Learning—0
Warren at SemEval-2020 Task 4: ALBERT and Multi-Task Learning for Commonsense Validation—0
Online Decision Based Visual Tracking via Reinforcement Learning—0
Soft-Robust Algorithms for Batch Reinforcement Learning—0
Optimizing the Neural Architecture of Reinforcement Learning AgentsCode0
UniCon: Universal Neural Controller For Physics-based Character Motion—0
IV-Posterior: Inverse Value Estimation for Interpretable Policy Certificates—0
Continuous Transition: Improving Sample Efficiency for Continuous Control Problems via MixUpCode0
Low-Bandwidth Communication Emerges Naturally in Multi-Agent Learning Systems—0
Cluster Based Deep Contextual Reinforcement Learning for top-k Recommendations—0
Hybrid Imitation Learning for Real-Time Service Restoration in Resilient Distribution Systems—0
Optimal Mixture Weights for Off-Policy Evaluation with Multiple Behavior Policies—0
Minimax Sample Complexity for Turn-based Stochastic Game—0
Offline Reinforcement Learning Hands-On—0
Human-Agent Cooperation in Bridge Bidding—0
Adaptable Automation with Modular Deep Reinforcement Learning and Policy Transfer—0
A survey of benchmarking frameworks for reinforcement learning—0
Deep Reinforcement Learning for Resource Constrained Multiclass Scheduling in Wireless Networks—0
Efficient Information Diffusion in Time-Varying Graphs through Deep Reinforcement LearningCode0
Reinforcement Learning-based Joint Path and Energy Optimization of Cellular-Connected Unmanned Aerial Vehicles—0
Real-time Active Vision for a Humanoid Soccer Robot Using Deep Reinforcement Learning—0
Predictive PER: Balancing Priority and Diversity towards Stable Deep Reinforcement Learning—0
Reinforcement Learning for Robust Missile Autopilot Design—0
Learning from Simulation, Racing in Reality—0
Exploring grid topology reconfiguration using a simple deep reinforcement learning approach—0
Diluted Near-Optimal Expert Demonstrations for Guiding Dialogue Stochastic Policy Optimisation—0
Auto Graph Encoder-Decoder for Neural Network Pruning—0
Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement LearningCode0
MetaSensing: Intelligent Metasurface Assisted RF 3D Sensing by Deep Reinforcement Learning—0
Towards Playing Full MOBA Games with Deep Reinforcement Learning—0
Uncertainty Estimation and Calibration with Finite-State Probabilistic RNNs—0
REPAINT: Knowledge Transfer in Deep Reinforcement Learning—0
PowerNet: Multi-agent Deep Reinforcement Learning for Scalable Powergrid Control—0
Solving The Lunar Lander Problem under Uncertainty using Reinforcement LearningCode0
A Reusable Framework Based on Reinforcement Learning to Design Antennas for Curved Surfaces—0
Learning Principle of Least Action with Reinforcement LearningCode0
Generative Adversarial Simulator—0
An analysis of Reinforcement Learning applied to Coach task in IEEE Very Small Size SoccerCode0
COCOI: Contact-aware Online Context Inference for Generalizable Non-planar Pushing—0
Consolidation via Policy Information Regularization in Deep RL for Multi-Agent Games—0
Logarithmic Regret for Reinforcement Learning with Linear Function Approximation—0
Path Design and Resource Management for NOMA enhanced Indoor Intelligent Robots—0
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Benchmark Results

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