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 3951–3975 of 15113 papers

TitleStatusHype
ASQ-IT: Interactive Explanations for Reinforcement-Learning Agents—0
Minimal Value-Equivalent Partial Models for Scalable and Robust Planning in Lifelong Reinforcement Learning—0
Constrained Reinforcement Learning for Dexterous ManipulationCode0
Explainable Deep Reinforcement Learning: State of the Art and Challenges—0
Forecaster-aided User Association and Load Balancing in Multi-band Mobile Networks—0
Model Based Reinforcement Learning with Non-Gaussian Environment Dynamics and its Application to Portfolio Optimization—0
Learning to View: Decision Transformers for Active Object Detection—0
The configurable tree graph (CT-graph): measurable problems in partially observable and distal reward environments for lifelong reinforcement learningCode0
Quasi-optimal Reinforcement Learning with Continuous Actions—0
Reinforcement learning-based estimation for partial differential equations—0
Multi-agent Reinforcement Learning with Graph Q-Networks for Antenna Tuning—0
Generative Slate Recommendation with Reinforcement Learning—0
Multi-Armed Bandits and Quantum Channel Oracles—0
Modeling Moral Choices in Social Dilemmas with Multi-Agent Reinforcement LearningCode0
Asynchronous Deep Double Duelling Q-Learning for Trading-Signal Execution in Limit Order Book Markets—0
Revisiting Estimation Bias in Policy Gradients for Deep Reinforcement Learning—0
Generalization through Diversity: Improving Unsupervised Environment Design—0
Domain-adapted Learning and Imitation: DRL for Power Arbitrage—0
Domain-adapted Learning and Interpretability: DRL for Gas Trading—0
A Survey of Meta-Reinforcement Learning—0
Tight Guarantees for Interactive Decision Making with the Decision-Estimation Coefficient—0
Advanced Scaling Methods for VNF deployment with Reinforcement Learning—0
Human-Timescale Adaptation in an Open-Ended Task Space—0
Multi-compartment Neuron and Population Encoding Powered Spiking Neural Network for Deep Distributional Reinforcement Learning—0
PIRLNav: Pretraining with Imitation and RL Finetuning for ObjectNavCode1
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Benchmark Results

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