Commit 5398c262 authored by MasterScrat's avatar MasterScrat
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Updated README

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...@@ -4,9 +4,11 @@ This repository contains reinforcement learning baselines for the [NeurIPS 2020 ...@@ -4,9 +4,11 @@ This repository contains reinforcement learning baselines for the [NeurIPS 2020
## Provided baselines ## Provided baselines
```{note} >>>
**Note:** looking for something simpler to start? We also provide a simpler Dueling Double DQN method implemented using PyTorch without relying on RLlib: **https://gitlab.aicrowd.com/flatland/flatland-examples** **Note:** looking for something simpler to start? We also provide a simpler Dueling Double DQN method implemented using PyTorch without relying on RLlib: **https://gitlab.aicrowd.com/flatland/flatland-examples**
``` >>>
Read the [baseline documentation](https://flatland.aicrowd.com/research/baselines.html) to see how the methods work in details.
### RL Methods ### RL Methods
...@@ -19,25 +21,13 @@ This repository contains reinforcement learning baselines for the [NeurIPS 2020 ...@@ -19,25 +21,13 @@ This repository contains reinforcement learning baselines for the [NeurIPS 2020
### Custom observations ### Custom observations
- Density observations - Density observations
- Local conflict observations - Combined observations
### Tricks ### Other approaches
- Action skipping - Action skipping
- Action masking - Action masking
## Organisation
Experiments consist of one or many RLlib YAML config files alongside a MARKDOWN file containing results, plots and a detailed description of the methodology.
All files are stored in a experiment folder under `experiments/<env-name>/<experiment-name>`.
- [Tree observations w/ fully connected network](experiments/flatland_random_sparse_small/tree_obs_fc_net)
- [Global observations w/ convnet](experiments/flatland_random_sparse_small/global_obs_conv_net)
## Setup ## Setup
The setup uses conda, [install it](https://www.anaconda.com/products/individual) if necessary. The setup uses conda, [install it](https://www.anaconda.com/products/individual) if necessary.
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