Via mapping one or more agents in your environments to (one or more) policies, multi-agent In python using Farama-Foundation’s gymnasium or DeepMind’s OpenSpiel, provide custom RLlib provides simple APIs to customize all aspects of your training- and experimental workflows.įor example, you may code your own environments Your existing system or learn how to further improve over it. Non-RL/ML) system? This branch of reinforcement learning is for you!Īlgorithms ( CQL, MARWIL, and DQfD), allowing you to either purely

Offline RL and imitation learning/behavior cloning: You don’t have a simulatorįor your particular problem, but tons of historic data recorded by a legacy (maybe If you want to get a quick preview of which algorithms and environments RLlib supports,Ĭlick on the dropdowns below: RLlib Algorithms

If you want to learn more about the RLlib training API,Īlso, see here for a simple example on how to write an action inference loop after training. You can also tweak RLlib’s default model config,and set up a separate config for evaluation. 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),

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