WebIt is proved that the resulting variance decays exponentially with the planning horizon as a function of the expansion policy, and the closer the resulting state transitions are to … WebDec 2, 2024 · Policy-gradient methods are widely used for learning control policies. They can be easily distributed to multiple workers and reach state-of-the-art results in many domains. Unfortunately, they...

[PDF] Softmax Policy Gradient Methods Can Take Exponential …

WebSoftTreeMax: Policy Gradient with Tree Search. no code yet • 28 Sep 2024 This allows us to reduce the variance of gradients by three orders of magnitude and to benefit from better sample complexity compared with standard policy gradient. WebOn Atari, SoftTreeMax demonstrates up to 5x better performance in faster run-time compared with distributed PPO. Policy-gradient methods are widely used for learning control policies. They can be easily distributed to multiple workers and reach state-of-the-art results in many domains. reading pubs

SoftTreeMax: Policy Gradient with Tree Search - slideslive.com

WebThis work introduces SoftTreeMax, the first approach that integrates tree-search into policy gradient, and leverages all gradients at the tree leaves in each environment step to reduce the variance of gradients by three orders of magnitude and to benefit from better sample complexity compared with standard policy gradient. Policy-gradient methods are widely … WebThis work introduces SoftTreeMax, the first approach that integrates tree-search into policy gradient, and leverages all gradients at the tree leaves in each environment step to reduce … WebOct 8, 2024 · These approaches have been mainly considered for value-based algorithms. Planning-based algorithms require a forward model and are computationally intensive at each step, but are more sample efficient. In this work, we introduce SoftTreeMax, the first approach that integrates tree-search into policy gradient. reading public schools budget