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https://github.com/osudrl/apex

A continuous deep reinforcement learning framework for robotics
https://github.com/osudrl/apex

Last synced: 2 months ago
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A continuous deep reinforcement learning framework for robotics

Host: GitHub
URL: https://github.com/osudrl/apex
Owner: osudrl
License: mit
Created: 2019-07-19T22:40:44.000Z (about 5 years ago)
Default Branch: master
Last Pushed: 2021-09-28T00:41:17.000Z (about 3 years ago)
Last Synced: 2024-04-27T23:39:16.369Z (5 months ago)
Language: Python
Homepage:
Size: 371 MB
Stars: 53
Watchers: 6
Forks: 31
Open Issues: 5
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

awesome-legged-locomotion-learning - [apex

README

----

Apex is a small, modular library that contains some implementations of continuous reinforcement learning algorithms. Fully compatible with OpenAI gym.

running1
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## Running experiments

### Basics
Any algorithm can be run from the apex.py entry point.

To run PPO on a cassie environment,

```bash
python apex.py ppo --env_name Cassie-v0 --num_procs 12 --run_name experiment01
```

To run TD3 on the gym environment Walker-v2,

```bash
python apex.py td3_async --env_name Walker-v2 --num_procs 12 --run_name experiment02
```

## Logging details / Monitoring live training progress
Tensorboard logging is enabled by default for all algorithms. The logger expects that you supply an argument named ```logdir```, containing the root directory you want to store your logfiles in, and an argument named ```seed```, which is used to seed the pseudorandom number generators.

A basic command line script illustrating this is:

```bash
python apex.py ars --logdir logs/ars --seed 1337
```

The resulting directory tree would look something like this:
```
trained_models/ # directory with all of the saved models and tensorboard logs
└── ars # algorithm name
└── Cassie-v0 # environment name
└── 8b8b12-seed1 # unique run name created with hash of hyperparameters
├── actor.pt # actor network for algo
├── critic.pt # critic network for algo
├── events.out.tfevents # tensorboard binary file
├── experiment.info # readable hyperparameters for this run
└── experiment.pkl # loadable pickle of hyperparameters
```

Using tensorboard makes it easy to compare experiments and resume training later on.

To see live training progress

Run ```$ tensorboard --logdir logs/``` then navigate to ```http://localhost:6006/``` in your browser

## Cassie Environments:
* `Cassie-v0` : basic unified environment for walking/running policies
* `CassieTraj-v0` : unified environment with reference trajectories
* `CassiePlayground-v0` : environment for executing autonomous missions
* `CassieStanding-v0` : environment for training standing policies

## Algorithms:
#### Currently implemented:
* Parallelism with [Ray](https://github.com/ray-project/ray)
* [GAE](https://arxiv.org/abs/1506.02438)/TD(lambda) estimators
* [PPO](https://arxiv.org/abs/1707.06347), VPG with ratio objective and with log likelihood objective
* [TD3](https://arxiv.org/abs/1802.09477) with [Parameter Noise Exploration](https://arxiv.org/abs/1706.01905)
* [DDPG](https://arxiv.org/abs/1509.02971)
* [RDPG](https://arxiv.org/abs/1512.04455)
* [ARS](https://arxiv.org/abs/1803.07055)
* Entropy based exploration bonus
* advantage centering (observation normalization WIP)

#### To be implemented long term:
* [SAC](https://arxiv.org/abs/1801.01290)
* [GPO](https://arxiv.org/abs/1711.01012)
* [NAF](https://arxiv.org/abs/1603.00748)
* [SVG](https://arxiv.org/abs/1510.09142)
* [I2A](https://arxiv.org/abs/1707.06203)
* [PGPE](http://ieeexplore.ieee.org/document/5708821/?reload=true)
* [Value Distribution](https://arxiv.org/pdf/1707.06887.pdf)
* Oracle methods (e.g. [GPS](https://arxiv.org/abs/1610.00529))
* CUDA support (should be trivial but I don't have a GPU to test on currently)

#### Maybe implemented in future:

* [DXNN](https://arxiv.org/abs/1008.2412)
* [ACER](https://arxiv.org/abs/1611.01224) and other off-policy methods
* Model-based methods

## Acknowledgements

Thanks to @ikostrikov's whose great implementations were used for debugging. Also thanks to @rll for rllab, which inspired a lot of the high level interface and logging for this library, and to @OpenAI for the original PPO tensorflow implementation. Thanks to @sfujim for the clean implementations of TD3 and DDPG in PyTorch. Thanks @modestyachts for the easy to understand ARS implementation.