Create an agent

rlberry requires you to use a very simple interface to write agents, with basically two methods to implement: fit() and eval().

The example below shows how to create an agent.

import numpy as np
from rlberry.agents import Agent

class MyAgent(Agent):
    name = "MyAgent"

    def __init__(
        self, env, param1=0.99, param2=1e-5, **kwargs
    ):  # it's important to put **kwargs to ensure compatibility with the base class
        # self.env is initialized in the base class
        # An evaluation environment is also initialized: self.eval_env
        Agent.__init__(self, env, **kwargs)

        self.param1 = param1
        self.param2 = param2

    def fit(self, budget, **kwargs):
        The parameter budget can represent the number of steps, the number of episodes etc,
        depending on the agent.
        * Interact with the environment (self.env);
        * Train the agent
        * Return useful information
        n_episodes = budget
        rewards = np.zeros(n_episodes)

        for ep in range(n_episodes):
            state, info = self.env.reset()
            done = False
            while not done:
                action = ...
                observation, reward, terminated, truncated, info = self.env.step(action)
                done = terminated or truncated
                rewards[ep] += reward

        info = {"episode_rewards": rewards}
        return info

    def eval(self, **kwargs):
        Returns a value corresponding to the evaluation of the agent on the
        evaluation environment.

        For instance, it can be a Monte-Carlo evaluation of the policy learned in fit().
        return 0.0


It’s important that your agent accepts optional **kwargs and pass it to the base class as Agent.__init__(self, env, **kwargs).

See also

Documentation of the classes Agent and AgentWithSimplePolicy.