epignatelli/helx-all

HELX: The RL experiments framework

Quickstart | Installation | Examples | Cite

What is HELX?

HELX is a JAX-based ecosystem that provides a standardised framework to run Reinforcement Learning experiments. With HELX you easily can:

• Use the helx.envs namespace to use the most common RL environments (gym, gymnax, dm_env, atari, ...)
• Use the helx.agents namespace to use the most common RL agents (DQN, PPO, SAC, ...)
• Use the helx.experiment namespace to run experiments on your local machine, on a cluster, or on the cloud
• Use the helx.base namespace to access the most common RL data structures and functions (e.g., a Ring buffer)

Each namespace provides a single, standardised interface to all agents, environments and experiment runners.

Installation

• Stable

Install the stable version of helx and its dependencies with:

pip install helx

• Nightly

Or, if you prefer to install the latest version from source:

pip install git+https://github.com/epignatelli/helx

Examples

A typical use case is to design an agent, and toy-test it on catch before evaluating it on more complex environments, such as atari, procgen or mujoco.

import bsuite
import gym

import helx.environment
import helx.experiment
import helx.agents

# create the enviornment in you favourite way
env = bsuite.load_from_id("catch/0")
# convert it to an helx environment
env = helx.environment.to_helx(env)
# create the agent
hparams = helx.agents.Hparams(env.obs_space(), env.action_space())
agent = helx.agents.Random(hparams)

# run the experiment
helx.experiment.run(env, agent, episodes=100)

Switching to a different environment is as simple as changing the env variable.

import bsuite
import gym

import helx.environment
import helx.experiment
import helx.agents

# create the enviornment in you favourite way
-env = bsuite.load_from_id("catch/0")
+env = gym.make("procgen:procgen-coinrun-v0")
# convert it to an helx environment
env = helx.environment.to_helx(env)
# create the agent
hparams = helx.agents.Hparams(env.obs_space(), env.action_space())
agent = helx.agents.Random(hparams)

# run the experiment
helx.experiment.run(env, agent, episodes=100)

Joining development

Adding a new agent (helx.agents.Agent)

An helx agent interface is designed as the minimal set of functions necessary to (i) interact with an environment and (ii) reinforcement learn.

from typing import Any
from jax import Array

from helx.base import Timestep
from helx.agents import Agent


class NewAgent(helx.agents.Agent):
    """A new RL agent."""
    def create(self, hparams: Any) -> None:
        """Initialises the agent's internal state (knowledge), such as a table,
        or some function parameters, e.g., the parameters of a neural network."""
        # implement me

    def init(self, key: KeyArray, timestep: Timestep) -> None:
        """Initialises the agent's internal state (knowledge), such as a table,
        or some function parameters, e.g., the parameters of a neural network."""
        # implement me

    def sample_action(
        self, agent_state: AgentState, obs: Array, *, key: KeyArray, eval: bool = False
    ):
        """Applies the agent's policy to the current timestep to sample an action."""
        # implement me

    def update(self, timestep: Timestep) -> Any:
        """Updates the agent's internal state (knowledge), such as a table,
        or some function parameters, e.g., the parameters of a neural network."""
        # implement me

Adding a new environment library (helx.environment.Environment)

To add a new library requires three steps:

1. Implement the helx.environment.Environment interface for the new library. See the dm_env implementation for an example.
2. Implement serialisation (to helx) of the following objects:
   • helx.environment.Timestep
   • helx.spaces.Discrete
   • helx.spaces.Continuous
3. Add the new library to the helx.environment.to_helx function to tell helx about the new protocol.

---

Cite

If you use helx please consider citing it as:

@misc{helx,
  author = {Pignatelli, Eduardo},
  title = {Helx: Interoperating between Reinforcement Learning Experimental Protocols},
  year = {2021},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/epignatelli/helx}}
  }

A note on maintainance

This repository was born as the recipient of personal research code that was developed over the years. Its maintainance is limited by the time and the resources of a research project resourced with a single person. Even if I would like to automate many actions, I do not have the time to maintain the whole body of automation that a well maintained package deserves. This is the reason of the WIP badge, which I do not plan to remove soon. Maintainance will prioritise the code functionality over documentation and automation.

Any help is very welcome. A quick guide to interacting with this repository:

• If you find a bug, please open an issue, and I will fix it as soon as I can.
• If you want to request a new feature, please open an issue, and I will consider it as soon as I can.
• If you want to contribute yourself, please open an issue first, let's discuss objective, plan a proposal, and open a pull request to act on it.

If you would like to be involved further in the development of this repository, please contact me directly at: edu dot pignatelli at gmail dot com.