From 3bf0eb254817c5464015fcfebaeecde0a2e7b3d7 Mon Sep 17 00:00:00 2001 From: Luis Pineda Date: Tue, 20 Apr 2021 00:20:55 -0400 Subject: [PATCH] Initial commit --- .gitattributes | 1 + .github/ISSUE_TEMPLATE/bug_report.md | 31 ++ .github/ISSUE_TEMPLATE/feature_request.md | 31 ++ .github/PULL_REQUEST_TEMPLATE.md | 29 ++ .github/workflows/ci.yml | 43 ++ .gitignore | 25 + .pre-commit-config.yaml | 28 ++ CHANGELOG.md | 5 + CODE_OF_CONDUCT.md | 76 +++ CONTRIBUTING.md | 31 ++ LICENSE | 21 + README.md | 157 ++++++ conf/algorithm/mbpo.yaml | 53 ++ conf/algorithm/pets.yaml | 30 ++ conf/dynamics_model/basic_ensemble.yaml | 18 + conf/dynamics_model/gaussian_mlp.yaml | 11 + .../dynamics_model/gaussian_mlp_ensemble.yaml | 13 + conf/main.yaml | 20 + conf/overrides/mbpo_ant.yaml | 25 + conf/overrides/mbpo_cartpole.yaml | 26 + conf/overrides/mbpo_halfcheetah.yaml | 26 + conf/overrides/mbpo_hopper.yaml | 26 + conf/overrides/mbpo_humanoid.yaml | 25 + conf/overrides/mbpo_inv_pendulum.yaml | 26 + conf/overrides/mbpo_pusher.yaml | 27 + conf/overrides/mbpo_walker.yaml | 26 + conf/overrides/pets_cartpole.yaml | 20 + conf/overrides/pets_halfcheetah.yaml | 23 + conf/overrides/pets_hopper.yaml | 21 + conf/overrides/pets_inv_pendulum.yaml | 21 + conf/overrides/pets_pusher.yaml | 21 + conf/overrides/pets_reacher.yaml | 21 + docs/conf.py | 61 +++ docs/index.rst | 76 +++ docs/logging.rst | 6 + docs/math.rst | 6 + docs/models.rst | 16 + docs/planning.rst | 6 + docs/replay_buffer.rst | 6 + docs/resources/dataset_evaluator.png | Bin 0 -> 246819 bytes docs/resources/halfcheetah-break.gif | Bin 0 -> 8941430 bytes docs/resources/inv_pendulum_mbpo_vis.gif | Bin 0 -> 1715482 bytes docs/util.rst | 10 + main.py | 28 ++ mbrl/__init__.py | 5 + mbrl/algorithms/mbpo.py | 271 ++++++++++ mbrl/algorithms/pets.py | 130 +++++ mbrl/constants.py | 10 + mbrl/diagnostics/README.md | 49 ++ mbrl/diagnostics/__init__.py | 9 + mbrl/diagnostics/control_env.py | 165 +++++++ mbrl/diagnostics/eval_model_on_dataset.py | 125 +++++ .../finetune_model_with_controller.py | 124 +++++ mbrl/diagnostics/visualize_model_preds.py | 303 ++++++++++++ mbrl/env/__init__.py | 7 + mbrl/env/ant_truncated_obs.py | 63 +++ mbrl/env/assets/cartpole.xml | 34 ++ mbrl/env/assets/half_cheetah.xml | 95 ++++ mbrl/env/assets/pusher.xml | 101 ++++ mbrl/env/assets/reacher3d.xml | 154 ++++++ mbrl/env/cartpole_continuous.py | 234 +++++++++ mbrl/env/humanoid_truncated_obs.py | 80 +++ mbrl/env/mujoco_envs.py | 10 + mbrl/env/mujoco_pixel_wrapper.py | 88 ++++ mbrl/env/pets_halfcheetah.py | 124 +++++ mbrl/env/pets_pusher.py | 69 +++ mbrl/env/pets_reacher.py | 103 ++++ mbrl/env/reward_fns.py | 42 ++ mbrl/env/termination_fns.py | 95 ++++ mbrl/models/__init__.py | 23 + mbrl/models/basic_ensemble.py | 274 +++++++++++ mbrl/models/gaussian_mlp.py | 377 ++++++++++++++ mbrl/models/model.py | 319 ++++++++++++ mbrl/models/model_env.py | 179 +++++++ mbrl/models/model_trainer.py | 231 +++++++++ mbrl/models/one_dim_tr_model.py | 318 ++++++++++++ mbrl/models/util.py | 85 ++++ mbrl/planning/__init__.py | 22 + mbrl/planning/core.py | 146 ++++++ mbrl/planning/sac_wrapper.py | 44 ++ mbrl/planning/trajectory_opt.py | 403 +++++++++++++++ mbrl/types.py | 46 ++ mbrl/util/__init__.py | 12 + mbrl/util/common.py | 400 +++++++++++++++ mbrl/util/logger.py | 220 +++++++++ mbrl/util/math.py | 298 +++++++++++ mbrl/util/mujoco.py | 356 ++++++++++++++ mbrl/util/replay_buffer.py | 445 +++++++++++++++++ notebooks/cem_rosenbrock_ex.ipynb | 156 ++++++ notebooks/fit_ensemble_1d.ipynb | 225 +++++++++ notebooks/fit_gaussian_mlp_ensemble_1d.ipynb | 214 ++++++++ notebooks/pets_example.ipynb | 464 ++++++++++++++++++ requirements/dev.txt | 7 + requirements/main.txt | 11 + requirements/mujoco.txt | 3 + setup.cfg | 22 + setup.py | 47 ++ tests/__init__.py | 4 + tests/algorithms/test_algorithms.py | 188 +++++++ tests/core/__init__.py | 4 + tests/core/test_common_utils.py | 242 +++++++++ tests/core/test_models.py | 341 +++++++++++++ tests/core/test_replay_buffer.py | 376 ++++++++++++++ tests/mujoco/test_diagnostics.py | 170 +++++++ tests/mujoco/test_util.py | 46 ++ 105 files changed, 10380 insertions(+) create mode 100644 .gitattributes create mode 100644 .github/ISSUE_TEMPLATE/bug_report.md create mode 100644 .github/ISSUE_TEMPLATE/feature_request.md create mode 100644 .github/PULL_REQUEST_TEMPLATE.md create mode 100644 .github/workflows/ci.yml create mode 100644 .gitignore create mode 100644 .pre-commit-config.yaml create mode 100644 CHANGELOG.md create mode 100644 CODE_OF_CONDUCT.md create mode 100644 CONTRIBUTING.md create mode 100644 LICENSE create mode 100644 README.md create mode 100644 conf/algorithm/mbpo.yaml create mode 100644 conf/algorithm/pets.yaml create mode 100644 conf/dynamics_model/basic_ensemble.yaml create mode 100644 conf/dynamics_model/gaussian_mlp.yaml create mode 100644 conf/dynamics_model/gaussian_mlp_ensemble.yaml create mode 100644 conf/main.yaml create mode 100644 conf/overrides/mbpo_ant.yaml create mode 100644 conf/overrides/mbpo_cartpole.yaml create mode 100644 conf/overrides/mbpo_halfcheetah.yaml create mode 100644 conf/overrides/mbpo_hopper.yaml create mode 100644 conf/overrides/mbpo_humanoid.yaml create mode 100644 conf/overrides/mbpo_inv_pendulum.yaml create mode 100644 conf/overrides/mbpo_pusher.yaml create mode 100644 conf/overrides/mbpo_walker.yaml create mode 100644 conf/overrides/pets_cartpole.yaml create mode 100644 conf/overrides/pets_halfcheetah.yaml create mode 100644 conf/overrides/pets_hopper.yaml create mode 100644 conf/overrides/pets_inv_pendulum.yaml create mode 100644 conf/overrides/pets_pusher.yaml create mode 100644 conf/overrides/pets_reacher.yaml create mode 100644 docs/conf.py create mode 100644 docs/index.rst create mode 100644 docs/logging.rst create mode 100644 docs/math.rst create mode 100644 docs/models.rst create mode 100644 docs/planning.rst create mode 100644 docs/replay_buffer.rst create mode 100644 docs/resources/dataset_evaluator.png create mode 100644 docs/resources/halfcheetah-break.gif create mode 100644 docs/resources/inv_pendulum_mbpo_vis.gif create mode 100644 docs/util.rst create mode 100644 main.py create mode 100644 mbrl/__init__.py create mode 100644 mbrl/algorithms/mbpo.py create mode 100644 mbrl/algorithms/pets.py create mode 100644 mbrl/constants.py create mode 100644 mbrl/diagnostics/README.md create mode 100644 mbrl/diagnostics/__init__.py create mode 100644 mbrl/diagnostics/control_env.py create mode 100644 mbrl/diagnostics/eval_model_on_dataset.py create mode 100644 mbrl/diagnostics/finetune_model_with_controller.py create mode 100644 mbrl/diagnostics/visualize_model_preds.py create mode 100644 mbrl/env/__init__.py create mode 100644 mbrl/env/ant_truncated_obs.py create mode 100644 mbrl/env/assets/cartpole.xml create mode 100644 mbrl/env/assets/half_cheetah.xml create mode 100644 mbrl/env/assets/pusher.xml create mode 100644 mbrl/env/assets/reacher3d.xml create mode 100644 mbrl/env/cartpole_continuous.py create mode 100644 mbrl/env/humanoid_truncated_obs.py create mode 100644 mbrl/env/mujoco_envs.py create mode 100644 mbrl/env/mujoco_pixel_wrapper.py create mode 100644 mbrl/env/pets_halfcheetah.py create mode 100644 mbrl/env/pets_pusher.py create mode 100644 mbrl/env/pets_reacher.py create mode 100644 mbrl/env/reward_fns.py create mode 100644 mbrl/env/termination_fns.py create mode 100644 mbrl/models/__init__.py create mode 100644 mbrl/models/basic_ensemble.py create mode 100644 mbrl/models/gaussian_mlp.py create mode 100644 mbrl/models/model.py create mode 100644 mbrl/models/model_env.py create mode 100644 mbrl/models/model_trainer.py create mode 100644 mbrl/models/one_dim_tr_model.py create mode 100644 mbrl/models/util.py create mode 100644 mbrl/planning/__init__.py create mode 100644 mbrl/planning/core.py create mode 100644 mbrl/planning/sac_wrapper.py create mode 100644 mbrl/planning/trajectory_opt.py create mode 100644 mbrl/types.py create mode 100644 mbrl/util/__init__.py create mode 100644 mbrl/util/common.py create mode 100644 mbrl/util/logger.py create mode 100644 mbrl/util/math.py create mode 100644 mbrl/util/mujoco.py create mode 100644 mbrl/util/replay_buffer.py create mode 100644 notebooks/cem_rosenbrock_ex.ipynb create mode 100644 notebooks/fit_ensemble_1d.ipynb create mode 100644 notebooks/fit_gaussian_mlp_ensemble_1d.ipynb create mode 100644 notebooks/pets_example.ipynb create mode 100644 requirements/dev.txt create mode 100644 requirements/main.txt create mode 100644 requirements/mujoco.txt create mode 100644 setup.cfg create mode 100644 setup.py create mode 100644 tests/__init__.py create mode 100644 tests/algorithms/test_algorithms.py create mode 100644 tests/core/__init__.py create mode 100644 tests/core/test_common_utils.py create mode 100644 tests/core/test_models.py create mode 100644 tests/core/test_replay_buffer.py create mode 100644 tests/mujoco/test_diagnostics.py create mode 100644 tests/mujoco/test_util.py diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 00000000..944bc9db --- /dev/null +++ b/.gitattributes @@ -0,0 +1 @@ +notebooks/* linguist-documentation \ No newline at end of file diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md new file mode 100644 index 00000000..98f1cfa5 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -0,0 +1,31 @@ +--- +name: "\U0001F41B Bug report" +about: Create a bug report to help us improve MBRL-Lib +title: "[Bug]" +labels: bug +assignees: '' + +--- + +## Steps to reproduce + + 1. _____ + 2. _____ + 3. _____ + +## Observed Results + + * What happened? This could be a description, log output, etc. + +## Expected Results + + * What did you expect to happen? + +## Relevant Code + + ``` + // TODO(you): code here to reproduce the problem + ``` + + + \ No newline at end of file diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md new file mode 100644 index 00000000..8f757939 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/feature_request.md @@ -0,0 +1,31 @@ +--- +name: "\U0001F680 Feature request" +about: Suggest an idea for a new feature in MBRL-Lib +title: "[Feature Request]" +labels: enhancement +assignees: '' + +--- + +# 🚀 Feature Request + + + +## Motivation + +**Is your feature request related to a problem? Please describe.** + + + +## Pitch + +**Describe the solution you'd like** + + +**Describe alternatives you've considered** + + +**Are you willing to open a pull request?** (See [CONTRIBUTING](../../CONTRIBUTING.md)) + +## Additional context +Add any other context or screenshots about the feature request here. \ No newline at end of file diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md new file mode 100644 index 00000000..e4375c9f --- /dev/null +++ b/.github/PULL_REQUEST_TEMPLATE.md @@ -0,0 +1,29 @@ +## Types of changes + + + +- [ ] Docs change / refactoring / dependency upgrade +- [ ] Bug fix (non-breaking change which fixes an issue) +- [ ] New feature (non-breaking change which adds functionality) +- [ ] Breaking change (fix or feature that would cause existing functionality to change) + +## Motivation and Context / Related issue + + + + + +## How Has This Been Tested (if it applies) + + + +## Checklist + + + + +- [ ] The documentation is up-to-date with the changes I made. +- [ ] I have read the [**CONTRIBUTING**](../../CONTRIBUTING.md) document and completed the CLA (see **CONTRIBUTING**). +- [ ] All tests passed, and additional code has been covered with new tests. + + \ No newline at end of file diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 00000000..93a883c1 --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,43 @@ +# This workflow will install Python dependencies, run tests and lint with a variety of Python versions +# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions + +name: CI + +on: + push: + branches: [ master ] + pull_request: + branches: [ master ] + +jobs: + build: + + runs-on: ubuntu-latest + strategy: + matrix: + python-version: [3.7, 3.8, 3.9] + + steps: + - uses: actions/checkout@v2 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v2 + with: + python-version: ${{ matrix.python-version }} + - name: Install dependencies + run: | + python -m pip install --upgrade pip + python -m pip install torch + python -m pip install -r requirements/main.txt + python -m pip install -r requirements/dev.txt + - name: Lint with flake8 + run: | + flake8 mbrl --ignore=E203,W503 --per-file-ignores='mbrl/env/mujoco_envs.py:F401 mbrl/diagnostics/__init__.py:F401 tests/*:F401' --max-line-length=100 + - name: Lint with mypy + run: | + mypy mbrl --no-strict-optional --ignore-missing-imports + - name: Check format with black + run: | + black --check mbrl + - name: Test with pytest + run: | + python -m pytest tests/core diff --git a/.gitignore b/.gitignore new file mode 100644 index 00000000..79bbd62b --- /dev/null +++ b/.gitignore @@ -0,0 +1,25 @@ +*personal* +.idea +.mypy_cache +notebooks/.ipynb_checkpoints +_build +__pycache__ + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 00000000..a8e217b6 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,28 @@ +repos: + - repo: https://github.com/psf/black + rev: 20.8b1 + hooks: + - id: black + language_version: python3.7 + + - repo: https://gitlab.com/pycqa/flake8 + rev: 3.7.9 + hooks: + - id: flake8 + additional_dependencies: [-e, "git+git://github.com/pycqa/pyflakes.git@1911c20#egg=pyflakes"] + + - repo: https://github.com/pre-commit/mirrors-mypy + rev: v0.812 + hooks: + - id: mypy + additional_dependencies: [torch, tokenize-rt==3.2.0] + args: [--no-strict-optional, --ignore-missing-imports] + exclude: setup.py + + - repo: https://github.com/pycqa/isort + rev: 5.5.2 + hooks: + - id: isort + files: 'mbrl/.*' + - id: isort + files: 'tests/.*' \ No newline at end of file diff --git a/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 00000000..20bc0566 --- /dev/null +++ b/CHANGELOG.md @@ -0,0 +1,5 @@ +# Changelog + +# v0.1.0 + +Initial release \ No newline at end of file diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md new file mode 100644 index 00000000..e26d1aba --- /dev/null +++ b/CODE_OF_CONDUCT.md @@ -0,0 +1,76 @@ +# Code of Conduct + +## Our Pledge + +In the interest of fostering an open and welcoming environment, we as +contributors and maintainers pledge to make participation in our project and +our community a harassment-free experience for everyone, regardless of age, body +size, disability, ethnicity, sex characteristics, gender identity and expression, +level of experience, education, socio-economic status, nationality, personal +appearance, race, religion, or sexual identity and orientation. + +## Our Standards + +Examples of behavior that contributes to creating a positive environment +include: + +* Using welcoming and inclusive language +* Being respectful of differing viewpoints and experiences +* Gracefully accepting constructive criticism +* Focusing on what is best for the community +* Showing empathy towards other community members + +Examples of unacceptable behavior by participants include: + +* The use of sexualized language or imagery and unwelcome sexual attention or + advances +* Trolling, insulting/derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or electronic + address, without explicit permission +* Other conduct which could reasonably be considered inappropriate in a + professional setting + +## Our Responsibilities + +Project maintainers are responsible for clarifying the standards of acceptable +behavior and are expected to take appropriate and fair corrective action in +response to any instances of unacceptable behavior. + +Project maintainers have the right and responsibility to remove, edit, or +reject comments, commits, code, wiki edits, issues, and other contributions +that are not aligned to this Code of Conduct, or to ban temporarily or +permanently any contributor for other behaviors that they deem inappropriate, +threatening, offensive, or harmful. + +## Scope + +This Code of Conduct applies within all project spaces, and it also applies when +an individual is representing the project or its community in public spaces. +Examples of representing a project or community include using an official +project e-mail address, posting via an official social media account, or acting +as an appointed representative at an online or offline event. Representation of +a project may be further defined and clarified by project maintainers. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported by contacting the project team at . All +complaints will be reviewed and investigated and will result in a response that +is deemed necessary and appropriate to the circumstances. The project team is +obligated to maintain confidentiality with regard to the reporter of an incident. +Further details of specific enforcement policies may be posted separately. + +Project maintainers who do not follow or enforce the Code of Conduct in good +faith may face temporary or permanent repercussions as determined by other +members of the project's leadership. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, +available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html + +[homepage]: https://www.contributor-covenant.org + +For answers to common questions about this code of conduct, see +https://www.contributor-covenant.org/faq \ No newline at end of file diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 00000000..b6f531f2 --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,31 @@ +# Contributing to MBRL-Lib +We want to make contributing to this project as easy and transparent as +possible. + +## Pull Requests +We actively welcome your pull requests. + +1. Fork the repo and create your branch from `master`. +2. If you've added code that should be tested, add tests. +3. If you've changed APIs, update the documentation. +4. Ensure the test suite passes. +5. Make sure your code lints. +6. If you haven't already, complete the Contributor License Agreement ("CLA"). + +## Contributor License Agreement ("CLA") +In order to accept your pull request, we need you to submit a CLA. You only need +to do this once to work on any of Facebook's open source projects. + +Complete your CLA here: + +## Issues +We use GitHub issues to track public bugs. Please ensure your description is +clear and has sufficient instructions to be able to reproduce the issue. + +Facebook has a [bounty program](https://www.facebook.com/whitehat/) for the safe +disclosure of security bugs. In those cases, please go through the process +outlined on that page and do not file a public issue. + +## License +By contributing to MBRL-Lib, you agree that your contributions will be licensed +under the LICENSE file in the root directory of this source tree. \ No newline at end of file diff --git a/LICENSE b/LICENSE new file mode 100644 index 00000000..b96dcb04 --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) Facebook, Inc. and its affiliates. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/README.md b/README.md new file mode 100644 index 00000000..710dbe4a --- /dev/null +++ b/README.md @@ -0,0 +1,157 @@ +[![Master](https://github.com/facebookresearch/mbrl-lib/workflows/CI/badge.svg)](https://github.com/facebookresearch/mbrl-lib/actions?query=workflow%3ACI) +[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/facebookresearch/mbrl-lib/tree/master/LICENSE) +[![Python 3.7+](https://img.shields.io/badge/python-3.7+-blue.svg)](https://www.python.org/downloads/release/python-360/) +[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) + + +# MBRL-Lib + +``mbrl-lib`` is a toolbox for facilitating development of +Model-Based Reinforcement Learning algorithms. It provides easily interchangeable +modeling and planning components, and a set of utility functions that allow writing +model-based RL algorithms with only a few lines of code. + +## Getting Started + +### Installation + +``mbrl-lib`` is a Python 3.7+ library. To install it, clone the repository, + + git clone https://github.com/facebookresearch/mbrl-lib.git + +then run + + cd mbrl-lib + pip install -e . + +If you are interested in contributing, please install the developer tools as well + + pip install -e ".[dev]" + +Finally, make sure your Python environment has +[PyTorch (>= 1.7)](https://pytorch.org) installed with the appropriate +CUDA configuration for your system. + +For testing your installation, run + + python -m pytest tests/core + python -m pytest tests/algorithms + +### Mujoco + +Mujoco is a popular library for testing RL methods. Installing Mujoco is not +required to use most of the components and utilities in MBRL-Lib, but if you +have a working Mujoco installation (and license) and want to test MBRL-Lib +on it, please run + + pip install -r requirements/mujoco.txt + +and to test our mujoco-related utilities, run + + python -m pytest tests/mujoco + +### Basic example +As a starting point, check out our [tutorial notebook](notebooks/pets_example.ipynb) +on how to write the PETS algorithm +([Chua et al., NeurIPS 2018](https://arxiv.org/pdf/1805.12114.pdf)) +using our toolbox, and running it on a continuous version of the cartpole +environment. + +## Provided algorithm implementations +MBRL-Lib provides implementations of popular MBRL algorithms +as examples of how to use this library. You can find them in the +[mbrl/algorithms](mbrl/algorithms) folder. Currently, we have implemented +[PETS](mbrl/algorithms/pets.py) and [MBPO](mbrl/algorithms/mbpo.py), and +we plan to keep increasing this list in the near future. + +The implementations rely on [Hydra](https://github.com/facebookresearch/hydra) +to handle configuration. You can see the configuration files in +[this](conf) folder. The [overrides](conf/overrides) subfolder contains +environment specific configurations for each environment, overriding the +default configurations with the best hyperparameter values we have found so far +for each combination of algorithm and environment. You can run training +by passing the desired override option via command line. +For example, to run MBPO on the gym version of HalfCheetah, you should call +```python +python main.py algorithm=mbpo overrides=mbpo_halfcheetah +``` +By default, all algorithms will save results in a csv file called `results.csv`, +inside a folder whose path looks like +`./exp/mbpo/default/gym___HalfCheetah-v2/yyyy.mm.dd/hhmmss`; +you can change the root directory (`./exp`) by passing +`root_dir=path-to-your-dir`, and the experiment sub-folder (`default`) by +passing `experiment=your-name`. The logger will also save a file called +`model_train.csv` with training information for the dynamics model. + +Beyond the override defaults, You can also change other configuration options, +such as the type of dynamics model +(e.g., `dynamics_model=basic_ensemble`), or the number of models in the ensemble +(e.g., `dynamics_model.model.ensemble_size=some-number`). To learn more about +all the available options, take a look at the provided +[configuration files](conf). + +Note that running the provided examples and `main.py` requires Mujoco, but +you can try out the library components (and algorithms) on other environments +by creating your own entry script and Hydra configuration. + +## Visualization tools +Our library also contains a set of +[visualization](mbrl/diagnostics) tools, meant to facilitate diagnostics and +development of models and controllers. These currently require Mujoco installation, but we are +planning to add more support and extensions in the future. Currently, +the following tools are provided: + +* [``Visualizer``](visualize_model_preds.py): Creates a video to qualitatively +assess model predictions over a rolling horizon. Specifically, it runs a + user specified policy in a given environment, and at each time step, computes + the model's predicted observation/rewards over a lookahead horizon for the + same policy. The predictions are plotted as line plots, one for each + observation dimension (blue lines) and reward (red line), along with the + result of applying the same policy to the real environment (black lines). + The model's uncertainty is visualized by plotting lines the maximum and + minimum predictions at each time step. The model and policy are specified + by passing directories containing configuration files for each; they can + be trained independently. The following gif shows an example of 200 steps + of pre-trained MBPO policy on Inverted Pendulum environment. + + ![Example of Visualizer](docs/resources/inv_pendulum_mbpo_vis.gif) + +* [``DatasetEvaluator``](eval_model_on_dataset.py): Loads a pre-trained model +and a dataset (can be loaded from separate directories), and computes + predictions of the model for each output dimension. The evaluator then + creates a scatter plot for each dimension comparing the ground truth output + vs. the model's prediction. If the model is an ensemble, the plot shows the + mean prediction as well as the individual predictions of each ensemble member. + + ![Example of DatasetEvaluator](docs/resources/dataset_evaluator.png) + +* [``FineTuner``](finetune_model_with_controller.py): Can be used to train a +model on a dataset produced by a given agent/controller. The model and agent + can be loaded from separate directories, and the fine tuner will roll the + environment for some number of steps using actions obtained from the + controller. The final model and dataset will then be saved under directory + "model_dir/diagnostics/subdir", where `subdir` is provided by the user. + +* [``True Dynamics Multi-CPU Controller``](control_env.py): This script can run +a trajectory optimizer agent on the true environment using Python's + multiprocessing. Each environment runs in its own CPU, which can significantly + speed up costly sampling algorithm such as CEM. The controller will also save + a video if the ``render`` argument is passed. Below is an example on + HalfCheetah-v2 using CEM for trajectory optimization. + + ![Control Half-Cheetah True Dynamics](docs/resources/halfcheetah-break.gif) + +Note that the tools above require Mujoco installation, and are specific to +models of type [``OneDimTransitionRewardModel``](../models/one_dim_tr_model.py). +We are planning to extend this in the future; if you have useful suggestions +don't hesitate to raise an issue or submit a pull request! + +## Documentation +Please check out our **[documentation](https://luisenp.github.io/mbrl-lib/)** +and don't hesitate to raise issues or contribute if anything is unclear! + +## License +`mbrl-lib` is released under the MIT license. See [LICENSE](LICENSE) for +additional details about it. See also our +[Terms of Use](https://opensource.facebook.com/legal/terms) and +[Privacy Policy](https://opensource.facebook.com/legal/privacy). diff --git a/conf/algorithm/mbpo.yaml b/conf/algorithm/mbpo.yaml new file mode 100644 index 00000000..8282e55d --- /dev/null +++ b/conf/algorithm/mbpo.yaml @@ -0,0 +1,53 @@ +# @package _group_ +name: "mbpo" + +normalize: true +target_is_delta: true +learned_rewards: true +freq_train_model: ${overrides.freq_train_model} + +sac_samples_action: true +initial_exploration_steps: 5000 +random_initial_explore: false +num_eval_episodes: 1 + +# -------------------------------------------- +# SAC Agent configuration +# -------------------------------------------- +agent: + _target_: pytorch_sac.agent.sac.SACAgent + obs_dim: ??? # to be specified later + action_dim: ??? # to be specified later + action_range: ??? # to be specified later + device: ${device} + critic_cfg: ${algorithm.double_q_critic} + actor_cfg: ${algorithm.diag_gaussian_actor} + discount: 0.99 + init_temperature: 0.1 + alpha_lr: ${overrides.sac_alpha_lr} + alpha_betas: [0.9, 0.999] + actor_lr: ${overrides.sac_actor_lr} + actor_betas: [0.9, 0.999] + actor_update_frequency: ${overrides.sac_actor_update_frequency} + critic_lr: ${overrides.sac_critic_lr} + critic_betas: [0.9, 0.999] + critic_tau: 0.005 + critic_target_update_frequency: ${overrides.sac_critic_target_update_frequency} + batch_size: 256 + learnable_temperature: true + target_entropy: ${overrides.sac_target_entropy} + +double_q_critic: + _target_: pytorch_sac.agent.critic.DoubleQCritic + obs_dim: ${algorithm.agent.obs_dim} + action_dim: ${algorithm.agent.action_dim} + hidden_dim: 1024 + hidden_depth: ${overrides.sac_hidden_depth} + +diag_gaussian_actor: + _target_: pytorch_sac.agent.actor.DiagGaussianActor + obs_dim: ${algorithm.agent.obs_dim} + action_dim: ${algorithm.agent.action_dim} + hidden_depth: ${overrides.sac_hidden_depth} + hidden_dim: 1024 + log_std_bounds: [-5, 2] \ No newline at end of file diff --git a/conf/algorithm/pets.yaml b/conf/algorithm/pets.yaml new file mode 100644 index 00000000..1736de09 --- /dev/null +++ b/conf/algorithm/pets.yaml @@ -0,0 +1,30 @@ +# @package _group_ +name: "pets" + +agent: + _target_: mbrl.planning.TrajectoryOptimizerAgent + action_lb: ??? + action_ub: ??? + planning_horizon: ${overrides.planning_horizon} + optimizer_cfg: ${algorithm.optimizer} + replan_freq: 1 + verbose: ${debug_mode} + +optimizer: + _target_: mbrl.planning.CEMOptimizer + num_iterations: ${overrides.cem_num_iters} + elite_ratio: ${overrides.cem_elite_ratio} + population_size: ${overrides.cem_population_size} + alpha: ${overrides.cem_alpha} + lower_bound: ??? + upper_bound: ??? + return_mean_elites: true + device: ${device} + +normalize: true +target_is_delta: true +initial_exploration_steps: ${overrides.trial_length} +freq_train_model: ${overrides.freq_train_model} +learned_rewards: ${overrides.learned_rewards} + +num_particles: 20 diff --git a/conf/dynamics_model/basic_ensemble.yaml b/conf/dynamics_model/basic_ensemble.yaml new file mode 100644 index 00000000..a1507bbc --- /dev/null +++ b/conf/dynamics_model/basic_ensemble.yaml @@ -0,0 +1,18 @@ +# @package _group_ +model: + _target_: mbrl.models.BasicEnsemble + ensemble_size: 5 + member_cfg: ${dynamics_model.member_cfg} + device: ${device} + propagation_method: fixed_model + +member_cfg: + _target_: mbrl.models.GaussianMLP + device: ${device} + num_layers: 4 + in_size: ??? + out_size: ??? + hid_size: 200 + use_silu: true + deterministic: false + diff --git a/conf/dynamics_model/gaussian_mlp.yaml b/conf/dynamics_model/gaussian_mlp.yaml new file mode 100644 index 00000000..785e1bd6 --- /dev/null +++ b/conf/dynamics_model/gaussian_mlp.yaml @@ -0,0 +1,11 @@ +# @package _group_ +model: + _target_: mbrl.models.GaussianMLP + device: ${device} + num_layers: 4 + in_size: ??? + out_size: ??? + ensemble_size: 1 + hid_size: 200 + use_silu: true + deterministic: false diff --git a/conf/dynamics_model/gaussian_mlp_ensemble.yaml b/conf/dynamics_model/gaussian_mlp_ensemble.yaml new file mode 100644 index 00000000..c4bf3007 --- /dev/null +++ b/conf/dynamics_model/gaussian_mlp_ensemble.yaml @@ -0,0 +1,13 @@ +# @package _group_ +model: + _target_: mbrl.models.GaussianMLP + device: ${device} + num_layers: 4 + in_size: ??? + out_size: ??? + ensemble_size: 7 + hid_size: 200 + use_silu: true + deterministic: false + propagation_method: random_model + learn_logvar_bounds: false # so far this works better diff --git a/conf/main.yaml b/conf/main.yaml new file mode 100644 index 00000000..873338f8 --- /dev/null +++ b/conf/main.yaml @@ -0,0 +1,20 @@ +defaults: + - algorithm: pets + - dynamics_model: gaussian_mlp_ensemble + - overrides: pets_cartpole + +seed: 0 +device: "cuda:0" +log_frequency_agent: 1000 +save_video: false +debug_mode: false + +experiment: default + +root_dir: "./exp" +hydra: + run: + dir: ${root_dir}/${algorithm.name}/${experiment}/${overrides.env}/${now:%Y.%m.%d}/${now:%H%M%S} + + sweep: + dir: ${root_dir}/${algorithm.name}/${experiment}/${overrides.env}/${now:%Y.%m.%d}/${now:%H%M%S} \ No newline at end of file diff --git a/conf/overrides/mbpo_ant.yaml b/conf/overrides/mbpo_ant.yaml new file mode 100644 index 00000000..ac569456 --- /dev/null +++ b/conf/overrides/mbpo_ant.yaml @@ -0,0 +1,25 @@ +# @package _group_ +env: "ant_truncated_obs" + +num_steps: 300000 +epoch_length: 1000 +num_elites: 5 +patience: 10 +model_lr: 0.0003 +model_wd: 0.00002 +model_batch_size: 256 +validation_ratio: 0.2 +freq_train_model: 250 +effective_model_rollouts_per_step: 400 +rollout_schedule: [20, 100, 1, 25] +num_sac_updates_per_step: 1 +sac_updates_every_steps: 20 +num_epochs_to_retain_sac_buffer: 1 + +sac_critic_lr: 0.0003 +sac_alpha_lr: 0.003 +sac_actor_lr: 0.0003 +sac_actor_update_frequency: 4 +sac_critic_target_update_frequency: 1 +sac_target_entropy: -3 +sac_hidden_depth: 2 diff --git a/conf/overrides/mbpo_cartpole.yaml b/conf/overrides/mbpo_cartpole.yaml new file mode 100644 index 00000000..09a92df8 --- /dev/null +++ b/conf/overrides/mbpo_cartpole.yaml @@ -0,0 +1,26 @@ +# @package _group_ +env: "cartpole_continuous" +trial_length: 200 + +num_steps: 5000 +epoch_length: 200 +num_elites: 5 +patience: 5 +model_lr: 0.001 +model_wd: 0.00005 +model_batch_size: 256 +validation_ratio: 0.2 +freq_train_model: 200 +effective_model_rollouts_per_step: 400 +rollout_schedule: [1, 15, 1, 1] +num_sac_updates_per_step: 20 +sac_updates_every_steps: 1 +num_epochs_to_retain_sac_buffer: 1 + +sac_critic_lr: 0.0003 +sac_actor_lr: 0.0003 +sac_alpha_lr: 0.0001 +sac_actor_update_frequency: 1 +sac_critic_target_update_frequency: 4 +sac_target_entropy: -0.05 +sac_hidden_depth: 2 diff --git a/conf/overrides/mbpo_halfcheetah.yaml b/conf/overrides/mbpo_halfcheetah.yaml new file mode 100644 index 00000000..360b6a23 --- /dev/null +++ b/conf/overrides/mbpo_halfcheetah.yaml @@ -0,0 +1,26 @@ +# @package _group_ +env: "gym___HalfCheetah-v2" +term_fn: "no_termination" + +num_steps: 400000 +epoch_length: 1000 +num_elites: 5 +patience: 5 +model_lr: 1.3e-3 +model_wd: 4e-6 +model_batch_size: 256 +validation_ratio: 0.2 +freq_train_model: 250 +effective_model_rollouts_per_step: 400 +rollout_schedule: [20, 150, 1, 1] +num_sac_updates_per_step: 1 +sac_updates_every_steps: 40 +num_epochs_to_retain_sac_buffer: 1 + +sac_alpha_lr: 3e-4 +sac_actor_lr: 3e-4 +sac_actor_update_frequency: 4 +sac_critic_lr: 3.7e-5 +sac_critic_target_update_frequency: 16 +sac_target_entropy: -3 +sac_hidden_depth: 2 diff --git a/conf/overrides/mbpo_hopper.yaml b/conf/overrides/mbpo_hopper.yaml new file mode 100644 index 00000000..3df47dbf --- /dev/null +++ b/conf/overrides/mbpo_hopper.yaml @@ -0,0 +1,26 @@ +# @package _group_ +env: "gym___Hopper-v2" +term_fn: "hopper" + +num_steps: 125000 +epoch_length: 1000 +num_elites: 5 +patience: 10 +model_lr: 0.001 +model_wd: 0.00005 +model_batch_size: 256 +validation_ratio: 0.2 +freq_train_model: 250 +effective_model_rollouts_per_step: 400 +rollout_schedule: [20, 150, 1, 15] +num_sac_updates_per_step: 20 +sac_updates_every_steps: 1 +num_epochs_to_retain_sac_buffer: 1 + +sac_critic_lr: 0.0003 +sac_actor_lr: 0.00003 +sac_alpha_lr: 0.0003 +sac_critic_target_update_frequency: 4 +sac_actor_update_frequency: 1 +sac_target_entropy: -1 +sac_hidden_depth: 2 diff --git a/conf/overrides/mbpo_humanoid.yaml b/conf/overrides/mbpo_humanoid.yaml new file mode 100644 index 00000000..3c146322 --- /dev/null +++ b/conf/overrides/mbpo_humanoid.yaml @@ -0,0 +1,25 @@ +# @package _group_ +env: "humanoid_truncated_obs" + +num_steps: 300000 +epoch_length: 1000 +num_elites: 5 +patience: 5 +model_lr: 5e-4 +model_wd: 3e-6 +model_batch_size: 256 +validation_ratio: 0.2 +freq_train_model: 250 +effective_model_rollouts_per_step: 400 +rollout_schedule: [20, 300, 1, 25] +num_sac_updates_per_step: 20 +sac_updates_every_steps: 1 +num_epochs_to_retain_sac_buffer: 5 + +sac_alpha_lr: 3e-4 +sac_actor_lr: 3e-4 +sac_actor_update_frequency: 16 +sac_critic_lr: 1e-5 +sac_critic_target_update_frequency: 8 +sac_target_entropy: -2 +sac_hidden_depth: 2 diff --git a/conf/overrides/mbpo_inv_pendulum.yaml b/conf/overrides/mbpo_inv_pendulum.yaml new file mode 100644 index 00000000..8a1a5fc7 --- /dev/null +++ b/conf/overrides/mbpo_inv_pendulum.yaml @@ -0,0 +1,26 @@ +# @package _group_ +env: "gym___InvertedPendulum-v2" +term_fn: "inverted_pendulum" + +num_steps: 20000 +epoch_length: 250 +num_elites: 5 +patience: 5 +model_lr: 0.001 +model_wd: 0.00005 +model_batch_size: 256 +validation_ratio: 0.2 +freq_train_model: 250 +effective_model_rollouts_per_step: 400 +rollout_schedule: [1, 15, 1, 1] +num_sac_updates_per_step: 20 +sac_updates_every_steps: 1 +num_epochs_to_retain_sac_buffer: 1 + +sac_critic_lr: 0.0003 +sac_actor_lr: 0.0003 +sac_alpha_lr: 0.0001 +sac_actor_update_frequency: 1 +sac_critic_target_update_frequency: 4 +sac_target_entropy: -0.05 +sac_hidden_depth: 2 diff --git a/conf/overrides/mbpo_pusher.yaml b/conf/overrides/mbpo_pusher.yaml new file mode 100644 index 00000000..029790c7 --- /dev/null +++ b/conf/overrides/mbpo_pusher.yaml @@ -0,0 +1,27 @@ +# @package _group_ +env: "pets_pusher" +term_fn: "no_termination" +trial_length: 150 + +num_steps: 20000 +epoch_length: 150 +num_elites: 5 +patience: 5 +model_lr: 0.001 +model_wd: 0.00005 +model_batch_size: 256 +validation_ratio: 0.2 +freq_train_model: 250 +effective_model_rollouts_per_step: 400 +rollout_schedule: [1, 15, 1, 1] +num_sac_updates_per_step: 20 +sac_updates_every_steps: 1 +num_epochs_to_retain_sac_buffer: 1 + +sac_critic_lr: 0.0003 +sac_actor_lr: 0.0003 +sac_alpha_lr: 0.0001 +sac_actor_update_frequency: 1 +sac_critic_target_update_frequency: 4 +sac_target_entropy: -0.05 +sac_hidden_depth: 2 diff --git a/conf/overrides/mbpo_walker.yaml b/conf/overrides/mbpo_walker.yaml new file mode 100644 index 00000000..9012bc96 --- /dev/null +++ b/conf/overrides/mbpo_walker.yaml @@ -0,0 +1,26 @@ +# @package _group_ +env: "gym___Walker2d-v2" +term_fn: "walker2d" + +num_steps: 300000 +epoch_length: 1000 +num_elites: 5 +patience: 10 +model_lr: 0.0003 +model_wd: 0.00002 +model_batch_size: 256 +validation_ratio: 0.2 +freq_train_model: 250 +effective_model_rollouts_per_step: 400 +rollout_schedule: [20, 150, 1, 1] +num_sac_updates_per_step: 20 +sac_updates_every_steps: 1 +num_epochs_to_retain_sac_buffer: 1 + +sac_critic_lr: 0.0003 +sac_actor_lr: 0.0003 +sac_alpha_lr: 0.003 +sac_critic_target_update_frequency: 1 +sac_actor_update_frequency: 4 +sac_target_entropy: -3 +sac_hidden_depth: 2 diff --git a/conf/overrides/pets_cartpole.yaml b/conf/overrides/pets_cartpole.yaml new file mode 100644 index 00000000..cf72496a --- /dev/null +++ b/conf/overrides/pets_cartpole.yaml @@ -0,0 +1,20 @@ +# @package _group_ +env: "cartpole_continuous" +learned_rewards: false +trial_length: 200 +num_steps: 5000 + +num_elites: 5 +model_lr: 7.5e-4 +model_wd: 3e-5 +model_batch_size: 256 +validation_ratio: 0 +freq_train_model: 50 +patience: 25 +num_epochs_train_model: 25 + +planning_horizon: 15 +cem_num_iters: 5 +cem_elite_ratio: 0.1 +cem_population_size: 350 +cem_alpha: 0.1 diff --git a/conf/overrides/pets_halfcheetah.yaml b/conf/overrides/pets_halfcheetah.yaml new file mode 100644 index 00000000..f166687e --- /dev/null +++ b/conf/overrides/pets_halfcheetah.yaml @@ -0,0 +1,23 @@ +# @package _group_ +env: "pets_halfcheetah" +term_fn: "no_termination" +obs_process_fn: mbrl.env.pets_halfcheetah.HalfCheetahEnv.preprocess_fn +learned_rewards: true +num_steps: 300000 +trial_length: 1000 + +num_elites: 5 +model_lr: 2e-4 +model_wd: 3e-5 +model_batch_size: 32 +validation_ratio: 0 +no_delta_list: [ 0 ] +freq_train_model: 1000 +patience: 25 +num_epochs_train_model: 25 + +planning_horizon: 30 +cem_num_iters: 5 +cem_elite_ratio: 0.1 +cem_population_size: 350 +cem_alpha: 0.1 \ No newline at end of file diff --git a/conf/overrides/pets_hopper.yaml b/conf/overrides/pets_hopper.yaml new file mode 100644 index 00000000..4e7e1d08 --- /dev/null +++ b/conf/overrides/pets_hopper.yaml @@ -0,0 +1,21 @@ +# @package _group_ +env: "gym___Hopper-v2" +term_fn: "hopper" +learned_rewards: true +num_steps: 125000 +trial_length: 1000 + +num_elites: 5 +model_lr: 2e-4 +model_wd: 3e-5 +model_batch_size: 32 +validation_ratio: 0 +freq_train_model: 250 +patience: 25 +num_epochs_train_model: 25 + +planning_horizon: 30 +cem_num_iters: 5 +cem_elite_ratio: 0.1 +cem_population_size: 350 +cem_alpha: 0.1 \ No newline at end of file diff --git a/conf/overrides/pets_inv_pendulum.yaml b/conf/overrides/pets_inv_pendulum.yaml new file mode 100644 index 00000000..ef9873fa --- /dev/null +++ b/conf/overrides/pets_inv_pendulum.yaml @@ -0,0 +1,21 @@ +# @package _group_ +env: "gym___InvertedPendulum-v2" +term_fn: "inverted_pendulum" +learned_rewards: true +trial_length: 1000 +num_steps: 20000 + +num_elites: 5 +model_lr: 00143 +model_wd: 0.000033 +model_batch_size: 256 +validation_ratio: 0.2 +freq_train_model: 250 +patience: 50 +num_epochs_train_model: 31 + +planning_horizon: 45 +cem_num_iters: 5 +cem_elite_ratio: 0.078 +cem_population_size: 480 +cem_alpha: 0.134484 diff --git a/conf/overrides/pets_pusher.yaml b/conf/overrides/pets_pusher.yaml new file mode 100644 index 00000000..61c9e345 --- /dev/null +++ b/conf/overrides/pets_pusher.yaml @@ -0,0 +1,21 @@ +# @package _group_ +env: "pets_pusher" +term_fn: "no_termination" +learned_rewards: true +trial_length: 150 +num_steps: 15000 + +num_elites: 5 +model_lr: 1e-5 +model_wd: 5e-4 +model_batch_size: 32 +validation_ratio: 0 +freq_train_model: 150 +patience: 25 +num_epochs_train_model: 25 + +planning_horizon: 25 +cem_num_iters: 5 +cem_elite_ratio: 0.1 +cem_population_size: 350 +cem_alpha: 0.1 diff --git a/conf/overrides/pets_reacher.yaml b/conf/overrides/pets_reacher.yaml new file mode 100644 index 00000000..f8990822 --- /dev/null +++ b/conf/overrides/pets_reacher.yaml @@ -0,0 +1,21 @@ +# @package _group_ +env: "pets_reacher" +learned_rewards: true +num_steps: 15000 +trial_length: 1000 + +num_elites: 5 +model_lr: 4e-4 +model_wd: 1.7e-4 +model_batch_size: 32 +validation_ratio: 0.05 +no_delta_list: [ 0 ] +freq_train_model: 250 +patience: 25 +num_epochs_train_model: 25 + +planning_horizon: 15 +cem_num_iters: 5 +cem_elite_ratio: 0.1 +cem_population_size: 350 +cem_alpha: 0.1 \ No newline at end of file diff --git a/docs/conf.py b/docs/conf.py new file mode 100644 index 00000000..15102e93 --- /dev/null +++ b/docs/conf.py @@ -0,0 +1,61 @@ +# Copyright (c) Facebook, Inc. and its affiliates. + +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +# Configuration file for the Sphinx documentation builder. +# +# This file only contains a selection of the most common options. For a full +# list see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +# -- Path setup -------------------------------------------------------------- + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. + +import os +import sys + +sys.path.insert(0, os.path.abspath("..")) + +# -- Project information ----------------------------------------------------- + +project = "MBRL-Lib" +copyright = "2021, Facebook AI Research" +author = "Facebook AI Research" + + +# -- General configuration --------------------------------------------------- + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = [ + "sphinx.ext.autodoc", + "sphinx.ext.coverage", + "sphinx.ext.napoleon", + "nbsphinx", +] + +# Add any paths that contain templates here, relative to this directory. +templates_path = ["_templates"] + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This pattern also affects html_static_path and html_extra_path. +exclude_patterns = ["_build", "Thumbs.db", ".DS_Store", "**.ipynb_checkpoints"] + + +# -- Options for HTML output ------------------------------------------------- + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. +# +html_theme = "sphinx_rtd_theme" + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +# html_static_path = ["_static"] diff --git a/docs/index.rst b/docs/index.rst new file mode 100644 index 00000000..47658062 --- /dev/null +++ b/docs/index.rst @@ -0,0 +1,76 @@ +Documentation for mbrl-lib +======================================== +``mbrl-lib`` is library to facilitate research on Model-Based Reinforcement Learning. + +Getting started +=============== + +Installation +------------ + +``mbrl-lib`` is a Python 3.7+ library. To install it, clone the repository, + +.. code-block:: bash + + git clone https://github.com/facebookresearch/mbrl-lib.git + +then run + +.. code-block:: bash + + cd mbrl-lib + pip install -e . + +If you also want the developer tools for contributing, run + +.. code-block:: bash + + pip install -e ".[dev]" + +Finally, make sure your Python environment has +`PyTorch (>= 1.7) `_ installed with the appropriate CUDA configuration +for your system. + + +To test your installation, run + +.. code-block:: bash + + python -m pytest tests/core + +Mujoco +------ +Mujoco is a popular library for testing RL methods. Installing Mujoco is not +required to use most of the components and utilities in MBRL-Lib, but if you +have a working Mujoco installation (and license) and want to test MBRL-Lib +on it, you please install + +.. code-block:: bash + + pip install -r requirements/mujoco.txt + +and to test our mujoco-related utilities, run + +.. code-block:: bash + + python -m pytest tests/mujoco + +Basic Example +------------- +As a starting point, check out our +`tutorial notebook `_ +on how to write the PETS algorithm +`(Chua et al., NeurIPS 2018) `_ +using our toolbox, and running it on a continuous version of the cartpole +environment. Then, please take a look at our API documentation below. + +.. toctree:: + :maxdepth: 3 + :caption: API Documentation + + models.rst + planning.rst + math.rst + util.rst + replay_buffer.rst + logging.rst diff --git a/docs/logging.rst b/docs/logging.rst new file mode 100644 index 00000000..d211a66f --- /dev/null +++ b/docs/logging.rst @@ -0,0 +1,6 @@ +Logging module +============== + +.. automodule:: mbrl.util.logger + :members: + :show-inheritance: diff --git a/docs/math.rst b/docs/math.rst new file mode 100644 index 00000000..ce13b246 --- /dev/null +++ b/docs/math.rst @@ -0,0 +1,6 @@ +Math utilities module +===================== + +.. automodule:: mbrl.util.math + :members: + :show-inheritance: diff --git a/docs/models.rst b/docs/models.rst new file mode 100644 index 00000000..75842e08 --- /dev/null +++ b/docs/models.rst @@ -0,0 +1,16 @@ +Models module +============= +This module provides implementations of common model architectures used in model-based RL, +including probabilistic and deterministic ensembles. All models in the library derive from +class :class:`mbrl.models.Model`. We provide a generic ensemble implementation, +:class:`mbrl.models.BasicEnsemble`, that can be used to produce epistemic uncertainty estimates +for any subclass of `Model`. For efficiency considerations, some specific model implementations +also provide their own ensemble implementations, without having to rely on BasicEnsemble. +One such model is :class:`mbrl.models.GaussianMLP`, which can be used as a single model or as +an ensemble. Additionally, it can be used as a deterministic model +trained with MSE loss, or a parameterized Gaussian with mean and log variance outputs, trained +with negative log-likelihood. + +.. automodule:: mbrl.models + :members: + :show-inheritance: diff --git a/docs/planning.rst b/docs/planning.rst new file mode 100644 index 00000000..125f3575 --- /dev/null +++ b/docs/planning.rst @@ -0,0 +1,6 @@ +Planning module +=============== + +.. automodule:: mbrl.planning + :members: + :show-inheritance: diff --git a/docs/replay_buffer.rst b/docs/replay_buffer.rst new file mode 100644 index 00000000..35aaf237 --- /dev/null +++ b/docs/replay_buffer.rst @@ -0,0 +1,6 @@ +Replay buffer module +==================== + +.. automodule:: mbrl.util.replay_buffer + :members: + :show-inheritance: diff --git a/docs/resources/dataset_evaluator.png b/docs/resources/dataset_evaluator.png new file mode 100644 index 0000000000000000000000000000000000000000..0190403c3463cc7d0a489611ceee5bb3a3d712b9 GIT binary patch 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