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Training a DQN Agent.

In this example, we will show you how to train an agent to search for specific target object and avoid obstacle simultaneously in Search-RrDoorDiscreteTest-v0 environement using Deep Q-Learning.

Dependences

To run this example, you should make sure that you have installed all the dependences. We recommend you to use anaconda to install and manage your python environment.

  • Keras==v1.2
  • Theano or thensorflow

To use Keras(v1.2), you should run

pip install keras==1.2

Please see this instruction to switch backend between Theano and Tensorflow

If you use the Theano backend, please see this instruction to config gpu.

If you use Tensorflowbackend, please set DEVICE_TF in constants.py to config gpu.

Training an agent

You can start the training process with default hyper parameters by running the following commands:

cd example/dqn
python run.py

You can change some parameteters in constants.py. if you set SHOW to True, You will see a window like this to monitor the agent while training:

show

  • While the Collision button turning red, a collision is detected.
  • While the Trigger button turning red, the agent is taking an action to ask the environment if it is seeing the target in a right place.

if you set Map to True, you will see a window showing the trajectory of the agent like this:

map

  • The green points represent where the agents realized that they had found a good view to observe the target object and got positive reward from the environment.At the same time, the episode is finished.
  • The purple points represent where collision detected collision, agents got negative reward. At the same time, the episode terminated.
  • The red points represent where the targets are.
  • The blue points represent where the agent start in a new episode.
  • The red lines represent the trajectories that the agents found taget object sucessfully in the end.
  • The black lines represent the trajectories of agents that did not find the target object in the end.
  • The blue line represents the trajectory of agent in the current episode.

You can change the architecture of DQN in dqn.py

Visualization

You can display a graph showing the history episode rewards by running the following script:

cd example/visualization
python reward.py -p ../dqn/log/monitor/tmp -a -d

reward

You can also display a graph showing the trajectory by running the following script:

cd example/visualization
python trajectory.py -p ../dqn/log/trajectory.csv

trajectory

  • The green points represent where the agents realized that they had found a good view to observe the target object and got positive reward from the environment.At the same time, the episode is finished.
  • The purple points represnet where collision detected collision, agents got negative reward. At the same time, the episode terminated.
  • The red lines represent the trajectories that the agents found taget object sucessfully in the end.
  • The black lines represent the trajectories of agents that did not find the target object in the end.