This repository contains Bayesian RL environments in OpenAI Gym API and BRL algorithms in OpenAI Baselines API.

It forks OpenAI Baselines' tf2 branch for supporting Tensorflow2.2 with Python 3.8.

Installation

Install Tensorflow 2.2.0

python -m pip install virtualenv # Install virtualenv. You can use conda instead.
virtualenv /path_to_venv/ -p python3.8
. /path_to_venv/bin/activate
python -m pip install tensorflow==2.2.0

Clone and install this repository

git clone https://github.com/personalrobotics/bayesian_rl.git
cd bayesian_rl
git submodule init
git submodule update
python -m pip install -r requirements.txt

Test your installation. You should be able to run commands in OpenAI Baslines, e.g.

python -m baselines.run --alg=ppo2 --env=bayes-ContinuousCartPole-v0 --num_timesteps=1e6

Training with BPO

To run Bayesian Policy Optimization algorithm, we need to provide two additional parameters, 1) --network=brl_mlp to use the BPO network as introduced in the paper, and 2) the size of observation dimension. The latter information is used by brl_mlp to split up the gym's per-step output into observation and belief.

For example, the following trains BPO agent on for cartpole control.

OPENAI_LOGDIR=~/models/cartpole/bpo OPENAI_LOG_FORMAT=tensorboard python -m baselines.run --alg=ppo2 --env=bayes-ContinuousCartPole-v0 --num_timesteps=2e7 --save_path=~/models/bpo-cartpole --network=brl_mlp --obs_dim=4 --num_env 2

To train without the two separate networks (BPO-), you can call ppo2 without specifying the network type (defaults to MLP).

OPENAI_LOGDIR=~/models/cartpole/bpo_minus OPENAI_LOG_FORMAT=tensorboard python -m baselines.run --alg=ppo2 --env=bayes-ContinuousCartPole-v0 --num_timesteps=1e6 --save_path=~/models/cartpole-ppo --num_env 2

The checkpoints are saved in OPENAI_LOGDIR and the checkpoints can be visualized by tensorboard:

tensorboard --logdir=~/models/cartpole/bpo_minus/tb

To load the latest checkpoint,

python -m baselines.run --alg=ppo2 --env=bayes-ContinuousCartPole-v0 --num_timesteps=0 --load_path=~/models/cartpole/bpo_minus/checkpoints --play

Bayes-Gym environments

See brl_gym for more detail.

BPO without an explicit Bayes Filter

This feature is temporarily disabled.

Without an explicit Bayes Filter, you can directly take observations and account for history by training on LSTM networks. LSTM would internally maintain a feature which encodes the history of observations.

OPENAI_LOGDIR=~/models/cartpole/ppo_lstm OPENAI_LOG_FORMAT=tensorboard python -m baselines.run --alg=ppo2 --env=ContinuousCartPole-v0 --num_timesteps=2e7 --save_path=~/models/cartpole/ppo_lstm_final --network=lstm --num_env 20 --nminibatches 20

Note that this may require more training than one with a Bayes Filter. The current LSTM policy requires num_env > nminibatches.

Please email Gilwoo Lee (gilwoo@uw.edu) for any bugs or questions.