SLM Lab

A research framework for Deep Reinforcement Learning using Unity, OpenAI Gym, PyTorch, Tensorflow.

Github Repo | Lab Documentation | Experiment Log Book

Features

This lab is for general deep reinforcement learning research, built with proper software engineering:

baseline algorithms
OpenAI gym, Unity environments
modular reusable components
multi-agents, multi-environments
scalable hyperparameter search with ray
useful graphs and analytics
fitness vector for universal benchmarking of agents, environments

Baselines

Work in progress.

The implemented baseline algorithms (besides research) are:

SARSA
DQN
Double DQN
REINFORCE
- Option to add entropy to encourage exploration
Actor-Critic
- Batch or episodic training
- Shared or separate actor and critic params
- Advantage calculated using n-step returns or generalized advantage estimation
- Option to add entropy to encourage exploration

Feature Demos

A multitask agent solving both OpenAI Cartpole-v0 and Unity Ball2D.

Experiment graph showing fitness from hyperparameter search.

Example total reward and loss graph from a session.

Read on for tutorials, research and results.

Github Repo | Lab Documentation | Experiment Log Book

Installation

Clone the SLM-Lab repo:

git clone https://github.com/kengz/SLM-Lab.git

Install dependencies (or inspect bin/setup_* first):

cd SLM-Lab/
bin/setup
yarn install
source activate lab

Alternatively, run the content of bin/setup_macOS or bin/setup_ubuntu on your terminal manually.

Setup

A config file config/default.json will be created.

{
  "data_sync_dir": "~/Dropbox/SLM-Lab/data",
  "plotly": {
    "username": "get from https://plot.ly/settings/api",
    "api_key": "generate from https://plot.ly/settings/api"
  }
}

update "data_sync_dir" if you run lab on remote and want to sync data for easy access; it will copy data/ there.
for plots, sign up for a free Plotly account and update the "plotly" JSON key.

Update

To update SLM Lab, pull the latest git commits and run update:

git pull
yarn update

Demo

Run the demo to quickly see the lab in action (and to test your installation).

It is VanillaDQN in CartPole-v0:

see slm_lab/spec/demo.json for example spec:

"dqn_cartpole": {
  "agent": [{
    "name": "VanillaDQN",
    "algorithm": {
      "name": "VanillaDQN",
      "action_policy": "boltzmann",
      "action_policy_update": "linear_decay",
      "gamma": 0.999,
      ...
    }
  }]
}

see config/experiments.json to schedule experiments:
```
"demo.json": {
  "dqn_cartpole": "train"
}
```
launch terminal in the repo directory, run the lab:
```
source activate lab
yarn start
```
This demo will run a single trial using the default parameters, and render the environment. After completion, check the output for data data/dqn_cartpole/. You should see a healthy session graph.
Next, change the run mode from "train" to "search" config/experiments.json, and rerun. This runs experiments of multiple trials with hyperparameter search. Environments will not be rendered.:
```
"demo.json": {
  "dqn_cartpole": "search"
}
```

If the quick start fails, consult Debugging.

Now the lab is ready for usage.

Read on: Github Repo | Lab Documentation | Experiment Log Book

Citing

If you use SLM-Lab in your research, you can cite it as follows:

@misc{kenggraesser2017slmlab,
    author = {Wah Loon Keng, Laura Graesser},
    title = {SLM-Lab},
    year = {2017},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\url{https://github.com/kengz/SLM-Lab}},
}

Contributing

If you're interested in using the lab for research, teaching or applications, please contact the authors.

bin913 / SLM-Lab