
The author explores the convergence and convergence rate of a natural policy gradient method with reusing historical trajectories via importance sampling, showing that it improves the convergence rate by an order of O(1/K).


coremsg

Reusing-Historical-Trajectories-in-Natural-Policy-Gradient-via-Importance-Sampling-Convergence-and-Convergence-Rate


Reusing Historical Trajectories in Natural Policy Gradient via Importance Sampling: Convergence and Convergence Rate


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Introducing Simple Policy Optimization (SPO) as a more efficient and stable alternative to traditional Proximal Policy Optimization (PPO) algorithms in reinforcement learning.


simple-policy-optimization-improving-sample-efficiency-and-stability-in-reinforcement-learning


Simple Policy Optimization: Improving Sample Efficiency and Stability in Reinforcement Learning
