From 900d269c89beec362156c7138f778be4ec3d9518 Mon Sep 17 00:00:00 2001
From: Erik Nygren <baerenjesus@gmail.com>
Date: Thu, 5 Sep 2019 15:41:33 +0000
Subject: [PATCH] Update flatland_2.0.md

---
 docs/flatland_2.0.md | 10 ++++------
 1 file changed, 4 insertions(+), 6 deletions(-)

diff --git a/docs/flatland_2.0.md b/docs/flatland_2.0.md
index 05982bab..21d59b4f 100644
--- a/docs/flatland_2.0.md
+++ b/docs/flatland_2.0.md
@@ -75,11 +75,11 @@ You can tune the following parameters:
 
 If you run into any bugs with sets of parameters please let us know.
 
-Here is a network with `realistic_mode=False` and the parameters from above.
+Here is a network with `grid_mode=False` and the parameters from above.
 
 ![sparse_random](https://i.imgur.com/Xg7nifF.png)
 
-and here with `realistic_mode=True`
+and here with `grid_mode=True`
 
 ![sparse_ordered](https://i.imgur.com/jyA7Pt4.png)
 
@@ -90,8 +90,9 @@ This is very common for railway networks where the initial plan usually needs to
 
 We implemted a poisson process to simulate delays by stopping agents at random times for random durations. The parameters necessary for the stochastic events can be provided when creating the environment.
 
-# Use a the malfunction generator to break agents from time to time
 ```
+# Use a the malfunction generator to break agents from time to time
+
 stochastic_data = {
     'prop_malfunction': 0.5,  # Percentage of defective agents
     'malfunction_rate': 30,  # Rate of malfunction occurence
@@ -124,9 +125,6 @@ for a in range(env.get_num_agents()):
     if info['malfunction'][a] == 0:
         action_dict.update({a: ...})
 
-```
-
-
 # Custom observation builder
 tree_observation = TreeObsForRailEnv(max_depth=2, predictor=ShortestPathPredictorForRailEnv())
 
-- 
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