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

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Showing 5651–5700 of 15113 papers

TitleStatusHype
Bridging the Gap Between Offline and Online Reinforcement Learning Evaluation Methodologies—0
Driver Assistance Eco-driving and Transmission Control with Deep Reinforcement Learning—0
Cross-Domain Transfer via Semantic Skill Imitation—0
Explaining Agent's Decision-making in a Hierarchical Reinforcement Learning Scenario—0
Efficient Exploration in Resource-Restricted Reinforcement Learning—0
Hierarchical Strategies for Cooperative Multi-Agent Reinforcement Learning—0
Quantum Control based on Deep Reinforcement Learning—0
Safety Correction from Baseline: Towards the Risk-aware Policy in Robotics via Dual-agent Reinforcement Learning—0
Reinforcement Learning in System Identification—0
Robust Policy Optimization in Deep Reinforcement LearningCode0
Scaling Marginalized Importance Sampling to High-Dimensional State-Spaces via State Abstraction—0
Model-Free Approach to Fair Solar PV Curtailment Using Reinforcement Learning—0
Single Cell Training on Architecture Search for Image Denoising—0
Scalable and Sample Efficient Distributed Policy Gradient Algorithms in Multi-Agent Networked Systems—0
PPO-UE: Proximal Policy Optimization via Uncertainty-Aware Exploration—0
Improving generalization in reinforcement learning through forked agents—0
A Review of Off-Policy Evaluation in Reinforcement Learning—0
A Survey on Reinforcement Learning Security with Application to Autonomous Driving—0
Evaluating Model-free Reinforcement Learning toward Safety-critical Tasks—0
Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes—0
VOQL: Towards Optimal Regret in Model-free RL with Nonlinear Function Approximation—0
Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes—0
Variance-Reduced Conservative Policy Iteration—0
Off-Policy Deep Reinforcement Learning Algorithms for Handling Various Robotic Manipulator Tasks—0
Generalization Through the Lens of Learning Dynamics—0
Hierarchical Deep Reinforcement Learning for VWAP Strategy Optimization—0
Effects of Spectral Normalization in Multi-agent Reinforcement LearningCode0
Leveraging Modality-specific Representations for Audio-visual Speech Recognition via Reinforcement Learning—0
Relate to Predict: Towards Task-Independent Knowledge Representations for Reinforcement Learning—0
Reinforcement Learning for Predicting Traffic Accidents—0
Near-Optimal Differentially Private Reinforcement Learning—0
Reinforcement Learning and Mixed-Integer Programming for Power Plant Scheduling in Low Carbon Systems: Comparison and Hybridisation—0
Reinforcement Learning for Resilient Power Grids—0
System Design for an Integrated Lifelong Reinforcement Learning Agent for Real-Time Strategy Games—0
Enhanced method for reinforcement learning based dynamic obstacle avoidance by assessment of collision risk—0
Confidence-Conditioned Value Functions for Offline Reinforcement Learning—0
A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces—0
Design and Planning of Flexible Mobile Micro-Grids Using Deep Reinforcement Learning—0
Accelerating Self-Imitation Learning from Demonstrations via Policy Constraints and Q-Ensemble—0
Selector-Enhancer: Learning Dynamic Selection of Local and Non-local Attention Operation for Speech Enhancement—0
Misspecification in Inverse Reinforcement Learning—0
What is the Solution for State-Adversarial Multi-Agent Reinforcement Learning?Code0
Understanding Self-Predictive Learning for Reinforcement Learning—0
State Space Closure: Revisiting Endless Online Level Generation via Reinforcement LearningCode0
Scalable Planning and Learning Framework Development for Swarm-to-Swarm Engagement Problems—0
Reinforcement Learning for Molecular Dynamics Optimization: A Stochastic Pontryagin Maximum Principle ApproachCode0
Safe Inverse Reinforcement Learning via Control Barrier Function—0
Switching to Discriminative Image Captioning by Relieving a Bottleneck of Reinforcement LearningCode0
Reinforcement Learning for UAV control with Policy and Reward Shaping—0
First Go, then Post-Explore: the Benefits of Post-Exploration in Intrinsic Motivation—0
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Benchmark Results

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