Updated README

README.md

@@ -4,9 +4,11 @@ This repository contains reinforcement learning baselines for the [NeurIPS 2020
 
 ## 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** 
-```
+>>>
+
+Read the [baseline documentation](https://flatland.aicrowd.com/research/baselines.html) to see how the methods work in details.
 
 ### RL Methods
 
@@ -19,25 +21,13 @@ This repository contains reinforcement learning baselines for the [NeurIPS 2020
 ### Custom observations
 
 - Density observations
-- Local conflict observations
+- Combined observations
 
-### Tricks
+### Other approaches
 
 - Action skipping
 - 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
 
 The setup uses conda, [install it](https://www.anaconda.com/products/individual) if necessary.