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 10426–10450 of 15113 papers

TitleStatusHype
Evolutionary Reinforcement Learning via Cooperative Coevolutionary Negatively Correlated Search—0
Energy Expenditure Estimation Through Daily Activity Recognition Using a Smart-phone—0
Bayesian Inverse Reinforcement Learning for Collective Animal MovementCode0
Induction and Exploitation of Subgoal Automata for Reinforcement Learning—0
Detecting and adapting to crisis pattern with context based Deep Reinforcement Learning—0
Deep Learning and Reinforcement Learning for Autonomous Unmanned Aerial Systems: Roadmap for Theory to Deployment—0
Active Learning of Causal Structures with Deep Reinforcement Learning—0
Driving Tasks Transfer in Deep Reinforcement Learning for Decision-making of Autonomous Vehicles—0
Robust Spoken Language Understanding with RL-based Value Error Recovery—0
PAC Reinforcement Learning Algorithm for General-Sum Markov Games—0
A Hybrid PAC Reinforcement Learning Algorithm—0
Visualizing the Loss Landscape of Actor Critic Methods with Applications in Inventory Optimization—0
Optimality-based Analysis of XCSF Compaction in Discrete Reinforcement LearningCode0
TAP-Net: Transport-and-Pack using Reinforcement Learning—0
Sparse Meta Networks for Sequential Adaptation and its Application to Adaptive Language Modelling—0
Vulnerability-Aware Poisoning Mechanism for Online RL with Unknown Dynamics—0
Adaptive Reinforcement Learning Model for Simulation of Urban Mobility during Crises—0
A reinforcement learning approach to hybrid control design—0
PlotThread: Creating Expressive Storyline Visualizations using Reinforcement Learning—0
Solving the single-track train scheduling problem via Deep Reinforcement Learning—0
Reinforcement Learning-based Black-Box Evasion Attacks to Link Prediction in Dynamic Graphs—0
Ranking Policy DecisionsCode0
Efficient Reinforcement Learning in Factored MDPs with Application to Constrained RL—0
Beyond variance reduction: Understanding the true impact of baselines on policy optimization—0
Data-driven Outer-Loop Control Using Deep Reinforcement Learning for Trajectory Tracking—0
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Benchmark Results

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