Metadata-Version: 2.1
Name: dinos
Version: 0.1.1
Summary: Active Learning Environments
Home-page: https://github.com/Pikalchemist/dinos
Author: Alexandre Manoury
Author-email: alex@pika.tf
License: UNKNOWN
Description: # Dinos
        
        Dinos is a simulation environment for active learning algorithms.
        
        
        # Getting started
        
        First of all, install the package either using pip:
        
            pip install dinos
        
        Or from the git repository:
        
            pip install -r ./requirements.txt
            pip install -e .
        
        Examples are provided in the `examples` folder from the git repository.
        
        
        # How does it works
        
        To run a Dinos experiment you need an `Environment` and an `Agent`.  
        For instance, an environment may be initialized as follow:
        
            from dinos.environments.playground import PlaygroundEnvironment
            env = PlaygroundEnvironment()
        
        From there you can either use your own code and use low level API to interact with the environment: `env.step(self, action, actionParameters=[], config=None)` as detailed later on. The second option is to use the Dinos Agent system to manage your algorithm.
        
        For instance to create an agent that will perform a random action at each step:
        
            from dinos.agents.random import RandomAgent
            agent = RandomAgent(env.world.findHost())
        
        > `env.world.findHost()` let you find an entity in the environment that can be controlled by your learner (we call such entity an *host*)
        
        Each `Agent` has a `reach(self, configOrGoal)` method that can be used to tell the agent to reach a specific goal.
        
        Additionally a specific type of agent exists: `Learner`. This class is designed to be used with a dataset or a memory to learn from its interactions with the environment.
        
        Each `Learner` has a `train(self, iterations=None, untilIteration=None, episodes=None, untilEpisode=None)` method used to train your learner for a given number of iterations or episodes.
        
        More details are present in the `examples` folder from the git repository.
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
