From c1039bc23053e569de1a984b966fff3b07c4a814 Mon Sep 17 00:00:00 2001
From: spiglerg <spiglerg@gmail.com>
Date: Thu, 23 May 2019 12:49:30 +0000
Subject: [PATCH] Update gettingstarted.rst
---
docs/gettingstarted.rst | 14 ++++++++++++--
1 file changed, 12 insertions(+), 2 deletions(-)
diff --git a/docs/gettingstarted.rst b/docs/gettingstarted.rst
index 0b7c552f..44741ff4 100644
--- a/docs/gettingstarted.rst
+++ b/docs/gettingstarted.rst
@@ -74,7 +74,7 @@ generated rail networks).
Environments can be rendered using the utils.rendertools utilities, for example:
-.. code-block: python
+.. code-block:: python
env_renderer = RenderTool(env, gl="QT")
env_renderer.renderEnv(show=True)
@@ -85,7 +85,7 @@ with a dictionary of actions whose keys are agents' handles (returned by
env.get_agent_handles() ) and the corresponding values the selected actions.
For example, for a 2-agents environment:
-.. code-block: python
+.. code-block:: python
handles = env.get_agent_handles()
action_dict = {handles[0]:0, handles[1]:0}
@@ -97,6 +97,16 @@ status for each agent. Further, the 'dones' dictionary returns an extra key
'__all__' that is set to True after all agents have reached their goals.
+In the specific case a TreeObsForRailEnv observation builder is used, it is
+possible to print a representation of the returned observations with the
+following code. Also, tree observation data is displayed by RenderTool by default.
+
+.. code-block:: python
+
+ for i in range(env.get_num_agents()):
+ env.obs_builder.util_print_obs_subtree(tree=obs[i], num_features_per_node=5)
+
+
Part 2 : Training a Simple DQN Agent
--------------
--
GitLab