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README.md

VariBAD

Code for the paper "VariBAD: A very good method for Bayes-Adaptive Deep RL via Meta-Learning" - Luisa Zintgraf, Kyriacos Shiarlis, Maximilian Igl, Sebastian Schulze, Yarin Gal, Katja Hofmann, Shimon Whiteson, published at ICLR 2020.

@inproceedings{zintgraf2020varibad,
  title={VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning},
  author={Zintgraf, Luisa and Shiarlis, Kyriacos and Igl, Maximilian and Schulze, Sebastian and Gal, Yarin and Hofmann, Katja and Whiteson, Shimon},
  booktitle={International Conference on Learning Representation (ICLR)},
  year={2020}}

Requirements

We use PyTorch for this code, and log results using TensorboardX.

The main requirements can be found in requirements.txt.

For the MuJoCo experiments, you need to install MuJoCo. Make sure you have the right MuJoCo version:

  • For the Cheetah and Ant environments, use mujoco150. (You can also use mujoco200 except for AntGoal, because there's a bug which leads to 80% of the env state being zero).
  • For Walker/Hopper, use mujoco131.

For mujoco131, use: gym==0.9.1 gym[mujoco]==0.9.1 mujoco-py==0.5.7

Overview

The main training loop for VariBAD can be found in metalearner.py, the models are in models/, the VAE set-up and losses are in vae.py and the RL algorithms in algorithms/.

There's quite a bit of documentation in the respective scripts so have a look there for details.

Running an experiment

To evaluate variBAD on the gridworld from the paper, run

python main.py --env-type gridworld_varibad

which will use hyperparameters from config/gridworld/args_grid_varibad.py.

To run variBAD on the MuJoCo experiments use:

python main.py --env-type cheetah_dir_varibad
python main.py --env-type cheetah_vel_varibad
python main.py --env-type ant_dir_varibad
python main.py --env-type walker_varibad

You can also run RL2 and the Oracle, just replace varibad above with the respective string. See main.py for all options.

The results will by default be saved at ./logs, but you can also pass a flag with an alternative directory using --results_log_dir /path/to/dir.

The default configs are in the config/ folder. You can overwrite any default hyperparameters using command line arguments.

Results will be written to tensorboard event files, and some visualisations will be printed every now and then.

Configs

Some comments on the flags in the config files:

  • You can choose what type of decoder you by setting the respective flags to true: --decode_reward True and/or --decode_state True.
  • You can also choose a task decoder (--decode_task True), which was proposed by Humplik et al. (2019). This method uses privileged information during meta-training (e.g., the task description or ID) to learn the posterior distribution in a supervised way. (Note that our implementation is based on the variBAD architecture, so differs slightly from theirs.)
  • The size of the latent dimension can be changed using --latent_dim.
  • In our experience, the performance of PPO depends a lot on the number of minibatches (--ppo_num_minibatch), the number of epochs (ppo_num_epochs), and the batchsize (change with --policy_num_steps and/or --num_processes). Another important parameter is the weight of the kl term (--kl_weight) in the ELBO.

Comments

  • When the flag disable_metalearner is activated, the file learner.py will be used instead of metalearner.py. This is a stripped down version without encoder, decoder, stochastic latent variables, etc. It can be used to train (belief) oracles or policies that are good on average.
  • For the environments do not use np.random (it's not thread safe) but stick to random or torch.random.
  • Currently, the VAE never looks at the starting state, but the prior is independent of where the agent starts. It was easier to implement like this. Since actions/rewards aren't available at the first time step, another option would be to just fill them with zeros.
  • I added an example environment with empty methods in environments/example_env.py, if you want to know what an environment should do.

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