The programming assignments cover topics that are important to perform research on combining machine learning and robots. They will cover:

• Behaviour cloning and imitation learning
• Model-free (PPO) and model-based RL methods (DDPG or PETS).
• Exploration and pretraining methods, such as HRL and Goal conditioned RL.
• Learning reward functions (VICE, un/semi-supervised RL)
• Offline reinforcement learning and Exploration

The assignments are also designed to familiarize students with the software needed to perform research:

• Deep learning libraries, such as pytorch or tensorflow
• Hardware constraints when working with real robots (power, compute, mechanical limits)
• Distributed computing for running proper experiments
• Visualization and analysis (the most important part)

The starter code for the assignment is given here.

Late Options:

• Your grade on the assignment may be reduced by 10% per day.