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 8701–8725 of 15113 papers

TitleStatusHype
Reinforced Hybrid Genetic Algorithm for the Traveling Salesman Problem—0
Offline reinforcement learning with uncertainty for treatment strategies in sepsis—0
Inferring Probabilistic Reward Machines from Non-Markovian Reward Processes for Reinforcement Learning—0
Attend2Pack: Bin Packing through Deep Reinforcement Learning with Attention—0
Learning Interaction-aware Guidance Policies for Motion Planning in Dense Traffic Scenarios—0
Aligning an optical interferometer with beam divergence control and continuous action spaceCode0
Policy Gradient Methods for Distortion Risk Measures—0
CLAIM: Curriculum Learning Policy for Influence Maximization in Unknown Social Networks—0
Adaptive Stress Testing for Adversarial Learning in a Financial Environment—0
Automated Gain Control Through Deep Reinforcement Learning for Downstream Radar Object Detection—0
Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learning—0
Enhancing Video Analytics Accuracy via Real-time Automated Camera Parameter Tuning—0
Adaptation of Quadruped Robot Locomotion with Meta-Learning—0
Computational Benefits of Intermediate Rewards for Goal-Reaching Policy LearningCode0
Sublinear Regret for Learning POMDPs—0
Towards Autonomous Pipeline Inspection with Hierarchical Reinforcement Learning—0
Quadruped Locomotion on Non-Rigid Terrain using Reinforcement Learning—0
Pseudo-Model-Free Hedging for Variable Annuities via Deep Reinforcement Learning—0
Federated Model Search via Reinforcement Learning—0
Learning Time-Invariant Reward Functions through Model-Based Inverse Reinforcement Learning—0
DORA: Toward Policy Optimization for Task-oriented Dialogue System with Efficient ContextCode0
A Unified Off-Policy Evaluation Approach for General Value Function—0
A Short Note on the Relationship of Information Gain and Eluder Dimension—0
Meta-Reinforcement Learning for Heuristic Planning—0
The Least Restriction for Offline Reinforcement Learning—0
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Benchmark Results

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