The framework config lets you choose between “tf2”, “tf” and “torch” for execution. In rollouts you can for instance specify the number of parallel workers to collect samples from the environment. Note that you can use any Farama-Foundation Gymnasium environment as env. build the algorithm, for _ in range ( 5 ): print ( algo. ![]() evaluation ( evaluation_num_workers = 1 ) ) algo = config. TensorFlow (both 1.x with static-graph and 2.x with eager mode) as well asĭepending on your needs, make sure to install either TensorFlow orįrom import PPOConfig config = ( # 1. RLlib does not automatically install a deep-learning framework, but supports It only takes a few steps to get your first RLlib workload RLlib is already used in production by industry leaders in many different verticals, Or own lots of pre-recorded, historic behavioral data to learn from, you will be If you either have your problem coded (in python) as an Purely from offline (historic) datasets, or using externallyĬonnected simulators, RLlib offers a simple solution for each of your decision ![]() Whether you would like to train your agents in a multi-agent setup, Unified and simple APIs for a large variety of industry applications. Production-level, highly distributed RL workloads while maintaining RLlib is an open-source library for reinforcement learning (RL),
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |