Unsupervised On-Policy Reinforcement Learning
This work combines Active Pre-Training with an On-Policy algorithm, Phasic Policy Gradient.
Active Pre-Training
Is used to pre-train a model free algorithm before defining a downstream task. It calculates the reward based on an estimatie of the particle based entropy of states. This reduces the training time if you want to define various tasks - i.e. robots for a warehouse.
Phasic Policy Gradient
Improved Version of Proximal Policy Optimization, which uses auxiliary epochs to train shared representations between the policy and a value network.
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Release files for unsupervised-on-policy 0.1.2
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