From f27e92371055a96efd2e91d07753953c65e6b63e Mon Sep 17 00:00:00 2001 From: Erik Nygren <erik.nygren@sbb.ch> Date: Thu, 23 May 2019 21:51:56 +0200 Subject: [PATCH] half baked version ;) --- docs/gettingstarted.rst | 34 +++++++++++++++++++++++++++++++++- 1 file changed, 33 insertions(+), 1 deletion(-) diff --git a/docs/gettingstarted.rst b/docs/gettingstarted.rst index 1ed2ed00..430b1f1e 100644 --- a/docs/gettingstarted.rst +++ b/docs/gettingstarted.rst @@ -112,8 +112,40 @@ examples/simple_example_3.py -Part 2 : Training a Simple DQN Agent +Part 2 : Training a Simple an Agent on Flatland -------------- +This is a brief tutorial on how to train an agent on Flatland. +Here we use a simple random agent to illustrate the process on how to interact with the environment. +The corresponding code can be found in examples/training_example.py and in the baselines repository +you find a tutorial to train a DQN agent to solve the navigation task. + +We start by importing the necessary Flatland libraries + +.. code-block:: python + +from flatland.envs.generators import complex_rail_generator +from flatland.envs.rail_env import RailEnv + +The complex_rail_generator is used in order to guarantee feasible railway network configurations for training. +Next we configure the difficulty of our task by modifying the complex_rail_generator parameters. + +..code-block:: python + +env = RailEnv(width=15, + height=15, + rail_generator=complex_rail_generator(nr_start_goal=10, nr_extra=10, min_dist=10, max_dist=99999, seed=0), + number_of_agents=5) + +The difficulty of a railway network depends on the dimensions (width x height) and the number of agents in the network. +By varying the number of start and goal connections (nr_start_goal) and the number of extra railway elements added (nr_extra) +the number of alternative paths of each agents can be modified. The more possible paths an agent has to reach its target the easier the task becomes. +Feel free to vary these parameters to see how your own agent holds up on different setting. The evalutation set of railway configurations will +cover the whole spectrum from easy to complex tasks. + +Once we are set with the environment we can load our preferred agent from either RLlib or any other ressource. Here we use a random agent to illustrate the code. +.. code-block:: python + +agent = RandomAgent(env., 4) -- GitLab