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 7326–7350 of 15113 papers

TitleStatusHype
Feature and Instance Joint Selection: A Reinforcement Learning Perspective—0
Feature-Based Aggregation and Deep Reinforcement Learning: A Survey and Some New Implementations—0
Feature-Based Interpretable Reinforcement Learning based on State-Transition Models—0
Feature Construction for Inverse Reinforcement Learning—0
Feature Engineering for Predictive Modeling using Reinforcement Learning—0
Feature-Rich Long-term Bitcoin Trading Assistant—0
Feature Selection as a Multiagent Coordination Problem—0
Feature Selection as a One-Player Game—0
Feature Selection Using Reinforcement Learning—0
Federated Double Deep Q-learning for Joint Delay and Energy Minimization in IoT networks—0
Federated Ensemble Model-based Reinforcement Learning in Edge Computing—0
Federated Learning-based Collaborative Wideband Spectrum Sensing and Scheduling for UAVs in UTM Systems—0
Federated Learning for Distributed Energy-Efficient Resource Allocation—0
Federated Model Search via Reinforcement Learning—0
Federated Multi-Agent Actor-Critic Learning for Age Sensitive Mobile Edge Computing—0
Federated Multi-Agent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multi-Microgrid Energy Management—0
Federated Natural Policy Gradient and Actor Critic Methods for Multi-task Reinforcement Learning—0
Federated Neuroevolution O-RAN: Enhancing the Robustness of Deep Reinforcement Learning xApps—0
Federated Offline Reinforcement Learning—0
Federated Offline Reinforcement Learning: Collaborative Single-Policy Coverage Suffices—0
Federated Reinforcement Distillation with Proxy Experience Memory—0
Federated Deep Reinforcement Learning—0
Federated Reinforcement Learning at the Edge—0
Federated Reinforcement Learning for Collective Navigation of Robotic Swarms—0
Federated Reinforcement Learning for Real-Time Electric Vehicle Charging and Discharging Control—0
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Benchmark Results

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