Vlearn: Off-Policy Learning with Efficient State-Value Function Estimation

Fabian Otto, Philipp Becker, Vien Anh Ngo, Gerhard Neumann

Primary Area: reinforcement learning

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Keywords: Reinforcement learning, Off-Policy, Trust Region Optimization, Value Function

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Abstract:

Existing off-policy reinforcement learning algorithms typically necessitate an explicit state-action-value function representation, which becomes problematic in high-dimensional action spaces.
These algorithms often encounter challenges where they struggle with the curse of dimensionality, as maintaining a state-action-value function in such spaces becomes data-inefficient.
In this work, we propose a novel off-policy trust region optimization approach, called Vlearn, that eliminates the requirement for an explicit state-action-value function.
Instead, we demonstrate how to efficiently leverage just a state-value function as the critic, thus overcoming several limitations of existing methods.
By doing so, Vlearn addresses the computational challenges posed by high-dimensional action spaces.
Furthermore, Vlearn introduces an efficient approach to address the challenges associated with pure state-value function learning in the off-policy setting.
This approach not only simplifies the implementation of off-policy policy gradient algorithms but also leads to consistent and robust performance across various benchmark tasks.
Specifically, by removing the need for a state-action-value function Vlearn simplifies the learning process and allows for more efficient exploration and exploitation in complex environments.

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Submission Number: 7690