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 2476–2500 of 15113 papers

TitleStatusHype
PolicyCleanse: Backdoor Detection and Mitigation in Reinforcement Learning—0
Backbones-Review: Feature Extraction Networks for Deep Learning and Deep Reinforcement Learning Approaches—0
Bach2Bach: Generating Music Using A Deep Reinforcement Learning Approach—0
A Modular and Transferable Reinforcement Learning Framework for the Fleet Rebalancing Problem—0
Adaptive Reinforcement Learning through Evolving Self-Modifying Neural Networks—0
A Modified Q-Learning Algorithm for Rate-Profiling of Polarization Adjusted Convolutional (PAC) Codes—0
B3C: A Minimalist Approach to Offline Multi-Agent Reinforcement Learning—0
A Model Selection Approach for Corruption Robust Reinforcement Learning—0
Adaptive Reinforcement Learning Model for Simulation of Urban Mobility during Crises—0
Continuously Learning Neural Dialogue Management—0
AWD3: Dynamic Reduction of the Estimation Bias—0
A model of discrete choice based on reinforcement learning under short-term memory—0
Avoiding Wireheading with Value Reinforcement Learning—0
A Model-free Learning Algorithm for Infinite-horizon Average-reward MDPs with Near-optimal Regret—0
Adaptive Reinforcement Learning for State Avoidance in Discrete Event Systems—0
Avoiding Negative Side-Effects and Promoting Safe Exploration with Imaginative Planning—0
Avoiding mode collapse in diffusion models fine-tuned with reinforcement learning—0
A Comparative Study of AI-based Intrusion Detection Techniques in Critical Infrastructures—0
Avoiding Jammers: A Reinforcement Learning Approach—0
Avoiding Catastrophic States with Intrinsic Fear—0
Adaptive Reinforcement Learning for Unobservable Random Delays—0
A Benchmarking Environment for Reinforcement Learning Based Task Oriented Dialogue Management—0
Continuous Input Embedding Size Search For Recommender Systems—0
Avoidance Learning Using Observational Reinforcement Learning—0
A Visual Communication Map for Multi-Agent Deep Reinforcement Learning—0
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Benchmark Results

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