softlearning is a reinforcement learning framework for training maximum entropy policies in continuous domains. softlearning is a deep reinforcement learning toolbox for training maximum entropy policies in continuous domains. it utilizes the tf.keras modules for most of the model classes (e.g. we use ray for the experiment orchestration. gcp or aws), and intelligently parallelize and distribute training for effective resource allocation. for docker installation you will need to have docker and docker compose installed. download and install mujoco 1.50 and 2.00 from the mujoco website.
unfortunately, gym and dm_control expect different paths for mujoco 2.00 installation, which is why you will need to have it installed both in ~/.mujoco/mujoco200_{platform} and ~/.mujoco/mujoco200. examples.development.main contains several different environments and there are more example scripts available in the /examples folder. for example, the previous example can be resumed as follows: soft actor-critic algorithms and applications. tuomas haarnoja*, aurick zhou*, kristian hartikainen*, george tucker, sehoon ha, jie tan, vikash kumar, henry zhu, abhishek gupta, pieter abbeel, and sergey levine. tuomas haarnoja*, kristian hartikainen*, pieter abbeel, and sergey levine. tuomas haarnoja, aurick zhou, pieter abbeel, and sergey levine. international conference on robotics and automation (icra), 2018. paper | videos reinforcement learning with deep energy-based policies. international conference on machine learning (icml), 2017. paper | videos softlearning is a reinforcement learning framework for training maximum entropy policies in continuous domains.
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