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Structured Action Reinforcement Learning (SARL)

⚠️ SARL is pre-release software, in active development!

SARL is a toolkit for performing reinforcement learning on unconventionally specified action spaces. Such action-spaces differ from the non-hierarchical, exclusively discrete or continuous action-spaces explored in conventional reinforcement learning.

Structures

Parameterized Actions

The most common structured action space is the parameterized action-space, requiring an agent to select from a discrete set of actions, then specify a continuous vector from that action's corresponding parameter-space (Masson et. al. 2016).

Contents

Algorithms

Name Inclusion Notes
H-PPO
MP-DQN
SP-DQN
P-DQN In Progress
PASVG(0)
PATRPO
PADDPG
Q-PAMDP In Progress

Environments

Name Inclusion Notes
Chase & Attack
Catching Point
King of Glory
Simulation / Moving
Half Field Offense (HFO)
Platform *
RoboCup *

* Custom wrappers exist, yet to be included in environment options.

Usage

Temporary state, subject to change.

Instructions for use on Linux, Mac OSX, or Windows (via WSL).

  • Ensure Python is installed python --version
  • Ensure Poetry is installed poetry --version
    • You can install poetry with curl -sSL https://install.python-poetry.org | python3 -
    • Full instructions and alternative methods can be found here
  • Clone this repository git clone https://github.com/ttollet/SARL.git
  • Change directory cd SARL
  • Install dependencies to virtual environment poetry install
  • Check functionality poetry run pytest

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Structured Action-space Reinforcement Learning

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