On the model-based stochastic value gradient for continuous reinforcement learning

Last update: Dec 15, 2022

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Overview

On the model-based stochastic value gradient for continuous reinforcement learning

This repository is by Brandon Amos, Samuel Stanton, Denis Yarats, and Andrew Gordon Wilson and contains the PyTorch source code to reproduce the experiments in our L4DC 2021 paper On model-based stochastic value gradient for continuous reinforcement learning. Videos of our agents are available here.

Setup and dependencies

After cloning this repository and installing PyTorch on your system, you can set up the code with:

python3 setup.py develop

A basic run and analysis

You can start a single local run on the humanoid with:

./train.py env=mbpo_humanoid

This will create an experiment directory in exp/local/<date>/ with models and logging info. Once that has saved out the first model, you can plot a video of the agent with some diagnostic information with the command:

./eval-vis-model.py exp/local/2021.05.07

Reproducing our main experimental results

We have the default hyper-parameters in this repo set to the best ones we found with a hyper-parameter search. The following command reproduces our final results using 10 seeds with the optimal hyper-parameter:

./train.py -m experiment=mbpo_final env=mbpo_cheetah,mbpo_hopper,mbpo_walker2d,mbpo_humanoid,mbpo_ant seed=$(seq -s, 10)

The results from this experiment can be plotted with our notebook nbs/mbpo.ipynb, which can also serve as a starting point for analyzing and developing further methods.

Reproducing our sweeps and ablations

Our main hyper-parameter sweeps are run with hydra's multi-tasking mode and can be launched with the following command after uncommenting the hydra/sweeper line in config/train.yaml:

./train.py -m experiment=full_poplin_sweep

The results from this experiment can be plotted with our notebook nbs/poplin.ipynb.

Citations

If you find this repository helpful for your publications, please consider citing our paper:

@inproceedings{amos2021svg,
  title={On the model-based stochastic value gradient for continuous reinforcement learning},
  author={Amos, Brandon and Stanton, Samuel and Yarats, Denis and Wilson, Andrew Gordon},
  booktitle={L4DC},
  year={2021}
}

Licensing

This repository is licensed under the CC BY-NC 4.0 License.

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Comments

Error running the code
Hi,

I'm trying to run the basic example in your code (on a local machine without slurm), and I'm encountering the following problems:

When I run ./train.py --run env=mbpo_cheetah, the first thing that happens is that temp_cfg, dx_cfg, actor_cfg, and critic_cfg are already initialized by Hydra before being passed to SACSVGAgent.__init__(...). Attempts to instantiate them using hydra.utils.instantiate cause an error. To get around this, I check the type and assign (using copy.deepcopy where necessary) the already-initialized to properties of SACSVGAgent.

The code keeps running once those changes are made, but it eventually throws this error when it reaches actor_loss.backward():

RuntimeError: CUDA error: CUBLAS_STATUS_EXECUTION_FAILED when calling `cublasSgemm(...)`

I'm guessing the cause is a version mismatch with Hydra, and my jury-rigged initialization is causing later problems. Could you please tell me what version of Hydra this code is compatible with, and what additional Hydra plugins I need to install?

Thanks!
opened by gauravmm 1
Adding Code of Conduct file

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On the model-based stochastic value gradient for continuous reinforcement learning

Related tags

Overview

On the model-based stochastic value gradient for continuous reinforcement learning

Setup and dependencies

A basic run and analysis

Reproducing our main experimental results

Reproducing our sweeps and ablations

Citations

Licensing

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Comments

Error running the code

Adding Code of Conduct file

Adding Contributing file

Owner

Facebook Research

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