A minimal reinforcement learning environment interface with additional opt-in features.
Author JuliaReinforcementLearning
8 Stars
Updated Last
10 Months Ago
Started In
June 2020


Stable Dev Build Status Coverage

This package is designed for two reasons:

  1. to provide compatibility between different reinforcement learning (RL) environment interfaces - for example, an algorithm that uses YourRLInterface should be able to use an environment from MyRLInterface without depending on MyRLInterface as long as they both support CommonRLInterface.
  2. to provide a very basic interface for users to write their own RL environments and algorithms.

To accomplish this, there is a single abstract environment type, AbstractEnv, a small required interface, and a larger optional interface will be added soon.

Required Interface

The interface has only three required functions:

step!(env, a)   # returns an observation, reward, done, and info
reset!(env)     # returns an observation
actions(env)    # returns the set of all possible actions for the environment

Optional Interface

In the near future, a number of optional interface functions will be added. Please file an issue if you would like to see a particular interface function.

Additional info

What does it mean for an RL Framework to "support" CommonRLInterface?

Suppose you have an abstract environment type in your package called YourEnv. Support for AbstractEnv means:

  1. You provide a convert methods

    convert(Type{YourEnv}, ::AbstractEnv)
    convert(Type{AbstractEnv}, ::YourEnv)

    If there are additional options in the conversion, you are encouraged to create and document constructors with additional arguments.

  2. You provide an implementation of the interface functions from your framework only using functions from CommonRLInterface

  3. You implement at minimum

    • CommonRL.reset!(::YourCommonEnv)
    • CommonRL.step!(::YourCommonEnv, a)
    • CommonRL.actions(::YourCommonEnv) and as many optional functions as you'd like to support, where YourCommonEnv is the concrete type returned by convert(Type{AbstractEnv}, ::YourEnv)

What does an environment implementation look like?

A 1-D LQR problem with discrete actions might look like this:

mutable struct LQREnv <: AbstractEnv

function CommonRLInterface.reset!(m::LQREnv)
    m.s = 0.0

function CommonRLInterface.step!(m::LQREnv, a)
    r = -m.s^2 - a^2
    sp = m.s = m.s + a + randn()
    return sp, r, false, NamedTuple()

CommonRLInterface.actions(m::LQREnv) = (-1.0, 0.0, 1.0)

# from version 0.2 on, you can implement optional functions like this:
# @provide CommonRLInterface.clone(m::LQREnv) = LQREnv(m.s)

What does a simulation with a random policy look like?

env = YourEnv()
done = false
o = reset!(env)
acts = actions(env)
rsum = 0.0
while !done
    o, r, done, info = step!(env, rand(acts)) 
    r += rsum
@show rsum

What does it mean for an algorithm to "support" CommonRLInterface?

You should have a method of your solver or algorithm that accepts a AbstractEnv, perhaps handling it by converting it to your framework first, e.g.

solve(env::AbstractEnv) = solve(convert(YourEnv, env))