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 9901–9950 of 15113 papers

TitleStatusHype
Semi-Supervised Off Policy Reinforcement Learning—0
The Architectural Implications of Distributed Reinforcement Learning on CPU-GPU Systems—0
Resolving Implicit Coordination in Multi-Agent Deep Reinforcement Learning with Deep Q-Networks & Game TheoryCode0
Emergence of Different Modes of Tool Use in a Reaching and Dragging Task—0
Efficient Reservoir Management through Deep Reinforcement Learning—0
Battery Model Calibration with Deep Reinforcement Learning—0
Selective Pseudo-Labeling with Reinforcement Learning for Semi-Supervised Domain Adaptation—0
Vehicular Cooperative Perception Through Action Branching and Federated Reinforcement Learning—0
Fever Basketball: A Complex, Flexible, and Asynchronized Sports Game Environment for Multi-agent Reinforcement Learning—0
Data Boost: Text Data Augmentation Through Reinforcement Learning Guided Conditional Generation—0
Multi-agent navigation based on deep reinforcement learning and traditional pathfinding algorithm—0
Neural Dynamic Policies for End-to-End Sensorimotor Learning—0
Offline Meta-level Model-based Reinforcement Learning Approach for Cold-Start Recommendation—0
Model-Agnostic Learning to Meta-Learn—0
Demonstration-efficient Inverse Reinforcement Learning in Procedurally Generated Environments—0
Emergent Complexity and Zero-shot Transfer via Unsupervised Environment DesignCode0
Dynamic RAN Slicing for Service-Oriented Vehicular Networks via Constrained Learning—0
DeepCrawl: Deep Reinforcement Learning for Turn-based Strategy Games—0
Designing a Prospective COVID-19 Therapeutic with Reinforcement Learning—0
Partially Connected Automated Vehicle Cooperative Control Strategy with a Deep Reinforcement Learning Approach—0
A Safe Reinforcement Learning Architecture for Antenna Tilt Optimisation—0
Pareto Deterministic Policy Gradients and Its Application in 5G Massive MIMO Networks—0
Sample Complexity of Policy Gradient Finding Second-Order Stationary Points—0
Coinbot: Intelligent Robotic Coin Bag Manipulation Using Deep Reinforcement Learning And Machine Teaching—0
Convergence Proof for Actor-Critic Methods Applied to PPO and RUDDER—0
Driving-Policy Adaptive Safeguard for Autonomous Vehicles Using Reinforcement Learning—0
Are Gradient-based Saliency Maps Useful in Deep Reinforcement Learning?—0
BSODA: A Bipartite Scalable Framework for Online Disease Diagnosis—0
Combining Cognitive Modeling and Reinforcement Learning for Clarification in Dialogue—0
Is Long Horizon RL More Difficult Than Short Horizon RL?—0
ExpanRL: Hierarchical Reinforcement Learning for Course Concept Expansion in MOOCs—0
EcoLight: Intersection Control in Developing Regions Under Extreme Budget and Network Constraints—0
Improving Neural Machine Translation for Sanskrit-English—0
Improving the Naturalness and Diversity of Referring Expression Generation models using Minimum Risk Training—0
Almost Optimal Model-Free Reinforcement Learningvia Reference-Advantage Decomposition—0
Assessing and Accelerating Coverage in Deep Reinforcement Learning—0
Instance-based Generalization in Reinforcement Learning—0
Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning—0
Answer-driven Deep Question Generation based on Reinforcement Learning—0
A Local Temporal Difference Code for Distributional Reinforcement Learning—0
A Learning-Exploring Method to Generate Diverse Paraphrases with Multi-Objective Deep Reinforcement Learning—0
A new convergent variant of Q-learning with linear function approximation—0
Can Temporal-Difference and Q-Learning Learn Representation? A Mean-Field Theory—0
Robust Multi-Agent Reinforcement Learning with Model Uncertainty—0
Promoting Stochasticity for Expressive Policies via a Simple and Efficient Regularization Method—0
Security Analysis of Safe and Seldonian Reinforcement Learning Algorithms—0
R-learning in actor-critic model offers a biologically relevant mechanism for sequential decision-making—0
RL Unplugged: A Collection of Benchmarks for Offline Reinforcement LearningCode0
Text Simplification with Reinforcement Learning Using Supervised Rewards on Grammaticality, Meaning Preservation, and Simplicity—0
On Efficiency in Hierarchical Reinforcement Learning—0
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Benchmark Results

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