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insight - Policy Gradient Methods
Analyzing Provable Policy Gradient Methods for Average-Reward Markov Potential Games
The authors analyze and propose algorithms for Markov potential games under the average reward criterion, demonstrating convergence to Nash equilibriums. They establish time complexity matching discounted reward settings.
MDP Homomorphic Policy Gradient Theorems for Continuous Control
Reinforcement learning on high-dimensional problems benefits from abstraction and MDP homomorphisms, enabling efficient policy optimization.
Provable Policy Gradient Methods for Average-Reward Markov Potential Games: Analyzing Convergence and Complexity
Globale Konvergenz von Policy Gradient Methoden für Markov-Potentialspiele mit durchschnittlicher Belohnung.
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