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 2451–2500 of 15113 papers

TitleStatusHype
Backward Imitation and Forward Reinforcement Learning via Bi-directional Model Rollouts—0
Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces—0
Backward Curriculum Reinforcement Learning—0
A Multiagent CyberBattleSim for RL Cyber Operation Agents—0
Contrasting Centralized and Decentralized Critics in Multi-Agent Reinforcement Learning—0
AMRL: Aggregated Memory For Reinforcement Learning—0
Backstepping Temporal Difference Learning—0
2048: Reinforcement Learning in a Delayed Reward Environment—0
Back-stepping Experience Replay with Application to Model-free Reinforcement Learning for a Soft Snake Robot—0
A Comparative Study of Deep Reinforcement Learning for Crop Production Management—0
Continuous-time Risk-sensitive Reinforcement Learning via Quadratic Variation Penalty—0
A Better Baseline for Second Order Gradient Estimation in Stochastic Computation Graphs—0
Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity—0
Backpropagation through Time and Space: Learning Numerical Methods with Multi-Agent Reinforcement Learning—0
Adaptive Reward-Poisoning Attacks against Reinforcement Learning—0
Continuous-time optimal investment with portfolio constraints: a reinforcement learning approach—0
A Modular Test Bed for Reinforcement Learning Incorporation into Industrial Applications—0
Backplay: 'Man muss immer umkehren'—0
Scene Induced Multi-Modal Trajectory Forecasting via Planning—0
Backdoors in DRL: Four Environments Focusing on In-distribution Triggers—0
MSPM: A Modularized and Scalable Multi-Agent Reinforcement Learning-based System for Financial Portfolio Management—0
Continuous-Time Reinforcement Learning: New Design Algorithms with Theoretical Insights and Performance Guarantees—0
Contrastive Diffuser: Planning Towards High Return States via Contrastive Learning—0
Control-Aware Representations for Model-based Reinforcement Learning—0
BACKDOORL: Backdoor Attack against Competitive Reinforcement Learning—0
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