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 10801–10825 of 15113 papers

TitleStatusHype
FastTD3: Simple, Fast, and Capable Reinforcement Learning for Humanoid Control—0
Fast Two-Time-Scale Stochastic Gradient Method with Applications in Reinforcement Learning—0
Fast Value Tracking for Deep Reinforcement Learning—0
Fault-Aware Robust Control via Adversarial Reinforcement Learning—0
Fault-Tolerant Control of Degrading Systems with On-Policy Reinforcement Learning—0
FDPP: Fine-tune Diffusion Policy with Human Preference—0
Fear the REAPER: A System for Automatic Multi-Document Summarization with Reinforcement Learning—0
Feasible Adversarial Robust Reinforcement Learning for Underspecified Environments—0
Feasible Policy Iteration for Safe Reinforcement Learning—0
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
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Benchmark Results

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