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 4461–4470 of 15113 papers

TitleStatusHype
Contact Energy Based Hindsight Experience Prioritization—0
SPOC: Imitating Shortest Paths in Simulation Enables Effective Navigation and Manipulation in the Real World—0
MASP: Scalable GNN-based Planning for Multi-Agent Navigation—0
RL-Based Cargo-UAV Trajectory Planning and Cell Association for Minimum Handoffs, Disconnectivity, and Energy Consumption—0
Score-Aware Policy-Gradient Methods and Performance Guarantees using Local Lyapunov Conditions: Applications to Product-Form Stochastic Networks and Queueing Systems—0
Training Reinforcement Learning Agents and Humans With Difficulty-Conditioned Generators—0
Integrated Drill Boom Hole-Seeking Control via Reinforcement Learning—0
Deep Reinforcement Learning for Community Battery Scheduling under Uncertainties of Load, PV Generation, and Energy Prices—0
Adaptive operator selection utilising generalised experience—0
Foundations for Transfer in Reinforcement Learning: A Taxonomy of Knowledge Modalities—0
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Benchmark Results

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