This repository contains the code of my Master's thesis "Training Neural Networks for Event-Based End-to-End Robot Control":

• Controller: Contains the controller code as well as Matplotlib plots.
• V-REP Scenarios: The V-REP scene files for 3 different lane following scenarios as well as the Lua script handling the communication between robot and controller via ROS can be found here.
• data.tar.gz: Contains all data
• TrainingNeuralNetworksForEventBasedEndToEndRobotControl_ClausMeschede.pdf: Thesis PDF file

In the recent past, reinforcement learning algorithms such as Deep Q-Learning (DQN) have gained attention due to their promise of solving complex decision making tasks in high-dimensional state spaces. Despite the successes, the amount of neurons and connections in deep Artifcial Neural Networks (ANN) makes applications computationally expensive, time-consuming and energetically inefficient, which is particularly problematic in real-time applications and mobile devices, e.g. autonomous robots. Event-based Spiking Neural Networks (SNN) performing computations on neuromorphic hardware could help solve some of these problems, although it is not clear yet how to train them efficiently, especially in reinforcement learning tasks. However, building energy-efficient mobile robots in the future could greatly benefit from SNNs with reinforcement learning capabilities. Therefore, a simulated lane following task was implemented in this thesis using a Dynamic Vision Sensor (DVS) for event-driven data processing in order to provide a testing ground for training algorithms. Hereby, the problem was approached from two different directions. First, a policy was learned using the DQN algorithm and transferred afterwards using supervised learning such that the control output could be computed by a SNN. Second, based on a Reward-modulated Spike-Timing-Dependent-Plasticity (R-STDP) learning rule a SNN was trained directly in the same task. Both approaches in this work have been shown to successfully follow the desired path in different scenarios with varying complexity. While after training the R-STDP-based controller shows a better performance, it lacks the reward-prediction capabilities needed for more complex decision making tasks. Until such an algorithm is found, indirect training using classical reinforcement learning methods could provide a practical workaround.

1. Start ROS node ("roscore").
2. Start V-REP with activated RosInterface (http://www.coppeliarobotics.com/helpFiles/en/rosTutorialIndigo.htm).
3. Load lane following scenario.
4. Start Simulation

1. Parameters: Before training all important parameters can be set in the parameters.py file. Most importantly, for each session a subfolder has to be created and named in the path variable where all the data is stored.
2. DQN training: Execute dqn_training.py to start the DQN training process. During training, the episode number and number of total steps are displayed in the console. In order to show rewards and episode length during training, start "tensorboard --logdir=<path>". When training is finished (maximum total steps reached), network weights as well as state-action dataset is stored in the dqn_data.h5 file.
3. DQN controller: After training, the DQN controller can be tested executing the dqn_controller.py file. The controller performs one lap on the outer lane of the course using the stored data. During the lap, the robot's position on the course, as well as its distance to the lane-center are recorded and stored afterwards in the dqn_performance_data.h5 file.
4. SNN training: For transferrng the previously learned DQN policy to a SNN, the snn_training.py executable uses the state-action dataset stored in the dqn_data.h5 file and trains another tensorflow neural network on it. Learned weights are stored in the snn_data.h5 file afterwards.
5. SNN controller: The weights stored in snn_data.h5 can be used by the SNN controller snn_controller.py. It performs one lap on the outer lane of the course and stores robot position and lane-center distance as well in snn_performance_data.h5.

1. Parameters: Before training all important parameters can be set in the parameters.py file. Most importantly, for each session a subfolder has to be created and named in the path variable where all the data is stored.
2. Braitenberg controller: The controller can be started by executing controller.py. It performs a single lap on the outer lane of the course and stores robot position as well as lane-center distance to braitenberg_performance_data.h5.

1. Parameters: Before training all important parameters can be set in the parameters.py file. Most importantly, for each session a subfolder has to be created and named in the path variable where all the data is stored.
2. R-STDP training: Execute training.py for training the R-STDP synapses. The training length can be adjusted in the parameters.py file. During training, network weights and termination positions are stored and saved in the rstdp_data.h5 file afterwards.
3. R-STDP controller: After training, the R-STDP controller can be tested executing the controller.py file. The controller performs one lap on the outer lane of the course using the previously learned weights. During the lap, the robot's position on the course, as well as its distance to the lane-center are recorded and stored afterwards in the rstdp_performance_data.h5 file.

• Ubuntu 14.04 LTS
• V-REP 3.3.2
• ROS Indigo 1.11.21
• Python 2.7
• Tensorflow 0.12.1
• NEST 2.10.0