(This repository is no longer being maintained.)

RL-Teacher-Atari

rl-teacher-atari is an extension of rl-teacher, which is in turn an implementation of of Deep Reinforcement Learning from Human Preferences [Christiano et al., 2017].

As-is, rl-teacher only handles MuJoCo environments. This repository is meant to extend that functionality to Atari environments and other complex Gym environments. Additionally, this repository extends and augments the code in the following ways:

• Full support for Gym Atari environments
• Added GA3C agent to optimize Atari and other complex environments
• Extended parallel_trpo to theoretically be able to handle environments with discrete action spaces
• Added save/load checkpoint functionality to reward models (and GA3C+database)
• Made human-feedback-api much more efficient by having humans sort clips into a red-black tree instead of doing blind comparisons
• Added a visualization of the sorting tree
• Simplified reward models by having the model minimize squared error between predicted reward and a real-number reward based on the ordering of clips in the tree
• Added support for frame-stacking
• Other miscellaneous improvements like speeding up pretraining, removing the multiprocess dependency from parallel-trpo, and adding the ability to define custom start-points in an Atari environment

Installation

The setup instructions are identical to rl-teacher except that you no longer need to set up MuJoCo unless you are trying to run MuJoCo environments, and you no longer need to install agents that are unused.

To run Atari specifically, use

cd ~/rl-teacher-atari
pip install -e .
pip install -e human-feedback-api
pip install -e agents/ga3c

Usage

To run rl-teacher-atari, use the same sorts of commands that you'd use for rl-teacher.

Examples:

python rl_teacher/teach.py -e Pong-v0 -n rl-test -p rl
python rl_teacher/teach.py -e Breakout-v0 -n synth-test -p synth -l 300
python rl_teacher/teach.py -e MontezumaRevenge-v0 -n human-test -p human -L 50

Note that with rl-teacher-atari you'll need far fewer labels. You'll also want to switch the agent back to parallel_trpo for solving MuJoCo environments.

python rl_teacher/teach.py -p rl -e ShortHopper-v1 -n base-rl -a parallel_trpo

There are a few new command-line arguments that are worth knowing about. Primarily, there are a set of four flags:

• --force_new_environment_clips
• --force_new_training_labels
• --force_new_reward_model
• --force_new_agent_model Activating these flags will erase the corresponding data from the disk/database. For the most part this won't be necessary, and you can simply pick a new experiment name. Note, however, that experiments within the same environment now share clips so you may want to --force_new_environment_clips when starting a new experiment in an old environment.

Also worth noting, there's a parameter called --stacked_frames (-f) that defaults to 4. This helps model movement that the human naturally sees in the video, but can alter how the system performs compared to rl-teacher. To remove frame stacking simply add -f 0 to the command-line arguments.

Backwards Compatibility

rl-teacher-atari is meant to be entirely backwards compatible, and do at least as well as rl-teacher on all tasks. If rl-teacher-atari lacks a feature that its parent has, please submit an issue.

TODO

• Fetch in clips added under previous experiments ("two") when an old experiment ("one") is re-launched!
• Get PPO agent(s) working
• Get all agents saving/loading cleanly
• Make the reward model select the right neural net based on the shape of the environment's observation space, rather than action space
• envs.py is still pretty gnarly; needs refactoring
• The red-black tree used for sorting is set up to allow pre-sorting, where a clip is assigned to a non-root node when created. Implement this!
• Get play.py into a better state