Mar 17, 2021 · R-BVE estimates the value of the behavior policy during training and only performs policy improvement at deployment time.
This work introduces Regularized Behavior Value Estimation (R-BVE), which estimates the value of the behavior policy during training and only performs ...
Mar 17, 2021 · Unlike most approaches, which use policy improvement during training, R-BVE estimates the value of the behavior policy during training and only ...
We introduce a new RL framework that is flexible to implement offline, online, off-policy and on-policy RL algorithms.
This article proposes an offline actor-critic with behavior value regularization (OAC-BVR) method.
May 21, 2024 · To tackle this issue, this article proposes an offline actor–critic with behavior value regularization (OAC-. BVR) method. In the policy ...
We also adopt the normalization of the Q function when calculating the actor loss, following the methodology outlined in Fujimoto & Gu (2021). This modification ...
Offline reinforcement learning (offline RL) aims to find task-solving policies from prerecorded datasets without online environment interaction.
Regularization is the technique for specifying constraints on the flexibility of a model, thereby reducing uncertainty in the estimated parameter values.
Our algorithm first trains the estimated behavior policy to obtain the behavior density. Then it turns to the actor-critic framework for policy training. 4 ...