ALPHAMEPOL

This repository contains the implementation of the ALPHAMEPOL algorithm, presented in Unsupervised Reinforcement Learning in Multiple Environments.

Installation

In order to use this codebase you need to work with a Python version >= 3.6. Moreover, you need to have a working setup of Mujoco with a valid Mujco license. To setup Mujoco, have a look here. To avoid any conflict with your existing Python setup, and to keep this project self-contained, it is suggested to work in a virtual environment with virtualenv. To install virtualenv:

pip install --upgrade virtualenv

Create a virtual environment, activate it and install the requirements:

virtualenv venv
source venv/bin/activate
pip install -r requirements.txt

Usage

Unsupervised Pre-Training

To reproduce the Unsupervised Pre-Training experiments in the paper, run:

./scripts/exploration/[gridworld_with_slope.sh | multigrid.sh | ant.sh | minigrid.sh]

Supervised Fine-Tuning

To reproduce the Supervised Fine-Tuning experiments, run:

./scripts/goal_rl/[gridworld_with_slope.sh | multigrid.sh | ant.sh | minigrid.sh]

By default, this will launch TRPO with ALPHAMEPOL initialization. To launch TRPO with a random initialization, simply omit the policy_init argument in the scripts.

Moreover, note that the scripts for the GridWorld with Slope and MultiGrid experiments have the argument num_goals = 50, meaning that the training will be performed with one goal at a time. If you want to speed up the process, you can use several processes (ideally one for each goal), by passing as argument num_goals = 1 and changing incrementally the seed. As regards the Ant and MiniGrid experiments, since the goals are predefined, you can also set the goal_index argument to specify a goal (from 0 to 7 and from 0 to 12 respectively).

Results Visualization

Once launched, each experiment will log statistics in the results folder. You can visualize everything by launching tensorboard targeting that directory:

python -m tensorboard.main --logdir=./results --port 8080