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Found 4 papers, 1 papers with code
Meta-learning the mirror map in policy mirror descent
Policy Mirror Descent (PMD) is a popular framework in reinforcement learning, serving as a unifying perspective that encompasses numerous algorithms.
A Novel Framework for Policy Mirror Descent with General Parameterization and Linear Convergence
Modern policy optimization methods in reinforcement learning, such as TRPO and PPO, owe their success to the use of parameterized policies.
Linear Convergence for Natural Policy Gradient with Log-linear Policy Parametrization
We analyze the convergence rate of the unregularized natural policy gradient algorithm with log-linear policy parametrizations in infinite-horizon discounted Markov decision processes.
Dimension-Free Rates for Natural Policy Gradient in Multi-Agent Reinforcement Learning
Cooperative multi-agent reinforcement learning is a decentralized paradigm in sequential decision making where agents distributed over a network iteratively collaborate with neighbors to maximize global (network-wide) notions of rewards.
