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Unsupervised On-Policy Reinforcement Learning\n\nThis work combines [Active Pre-Training](https://arxiv.org/abs/2103.04551) with an On-Policy algorithm, [Phasic Policy Gradient](https://arxiv.org/abs/2009.04416).\n\n## Active Pre-Training\n\nIs 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. \n\n## Phasic Policy Gradient\n\nImproved Version of Proximal Policy Optimization, which uses auxiliary epochs to train shared representations between the policy and a value network.\n\n\n## Installation\n\nInstall via pip `pip install unsupervised-on-policy`.\nConfigure by changing values in the config at `run_train.py`. \n\n## Run\n\nSupports the key words `load` `skip_pretrain` and `model_path`. \nExample usage:\n\n`python3 -m unsupervised_on_policy.run_train --skip_pretrain --prefix test \n--model_path /home/lars/apt_ppg`","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftnfru%2Funsupervised-on-policy","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftnfru%2Funsupervised-on-policy","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftnfru%2Funsupervised-on-policy/lists"}