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 3951–4000 of 15113 papers

TitleStatusHype
AutoCost: Evolving Intrinsic Cost for Zero-violation Reinforcement Learning—0
Minimal Value-Equivalent Partial Models for Scalable and Robust Planning in Lifelong Reinforcement Learning—0
SMART: Self-supervised Multi-task pretrAining with contRol Transformers—0
Story Shaping: Teaching Agents Human-like Behavior with Stories—0
Model Based Reinforcement Learning with Non-Gaussian Environment Dynamics and its Application to Portfolio Optimization—0
Forecaster-aided User Association and Load Balancing in Multi-band Mobile Networks—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
Modeling Moral Choices in Social Dilemmas with Multi-Agent Reinforcement LearningCode0
Multi-Armed Bandits and Quantum Channel Oracles—0
Revisiting Estimation Bias in Policy Gradients for Deep Reinforcement Learning—0
Generative Slate Recommendation with Reinforcement Learning—0
Asynchronous Deep Double Duelling Q-Learning for Trading-Signal Execution in Limit Order Book Markets—0
Domain-adapted Learning and Imitation: DRL for Power Arbitrage—0
Advanced Scaling Methods for VNF deployment with Reinforcement Learning—0
Generalization through Diversity: Improving Unsupervised Environment Design—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
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
Human-Timescale Adaptation in an Open-Ended Task Space—0
A reinforcement learning path planning approach for range-only underwater target localization with autonomous vehiclesCode1
DQNAS: Neural Architecture Search using Reinforcement Learning—0
Learning to solve arithmetic problems with a virtual abacus—0
Heterogeneous Multi-Robot Reinforcement LearningCode2
Adversarial Robust Deep Reinforcement Learning Requires Redefining Robustness—0
Sim-Anchored Learning for On-the-Fly AdaptationCode0
Show me what you want: Inverse reinforcement learning to automatically design robot swarms by demonstration—0
Neuro-Symbolic World Models for Adapting to Open World Novelty—0
Neuro-symbolic Meta Reinforcement Learning for Trading—0
CogReact: A Reinforced Framework to Model Human Cognitive Reaction Modulated by Dynamic Intervention—0
Reinforcement Learning for Protocol Synthesis in Resource-Constrained Wireless Sensor and IoT Networks—0
PRUDEX-Compass: Towards Systematic Evaluation of Reinforcement Learning in Financial Markets—0
Risk-Averse Reinforcement Learning via Dynamic Time-Consistent Risk Measures—0
Deep-Reinforcement-Learning-based Path Planning for Industrial Robots using Distance Sensors as ObservationCode1
First Three Years of the International Verification of Neural Networks Competition (VNN-COMP)—0
Decentralized model-free reinforcement learning in stochastic games with average-reward objective—0
Hierarchical Deep Q-Learning Based Handover in Wireless Networks with Dual Connectivity—0
A Constrained-Optimization Approach to the Execution of Prioritized Stacks of Learned Multi-Robot Tasks—0
Multi-Target Landmark Detection with Incomplete Images via Reinforcement Learning and Shape Prior—0
Risk Sensitive Dead-end Identification in Safety-Critical Offline Reinforcement Learning—0
Mutation Testing of Deep Reinforcement Learning Based on Real FaultsCode0
Safe Policy Improvement for POMDPs via Finite-State Controllers—0
Reinforcement Learning-based Joint Handover and Beam Tracking in Millimeter-wave Networks—0
Predictive World Models from Real-World Partial ObservationsCode0
Asynchronous training of quantum reinforcement learning—0
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Benchmark Results

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