Model-based Reinforcement Learning

|-- envs
    |-- 2Dpusher_env.py # Implementation of the pusher environment 
    |-- __init__.py # Register two pusher environmnets
|-- run.py # Entry of all the experiments. Also check out the commented code in this file.
|-- mpc.py # MPC to optimize model; Call CEM from this file
|-- model.py # Creat and train the ensemble dynamics model
|-- agent.py # An agent that can be used to interact with the environment
             # and a random policy
|-- util.py # Some utility functions we kindly provide. You do not have to use them.
|-- opencv_draw.py # Contains features for rendering the Box2D environments. You don't have to do anything to this file.

Prerequisite

Run pip install opencv-python

Guidelines for Implementation

• When implementing the MPC class, use the mpc_params that is passed into this class.
• One tip is to write a separate CEMOptimizer and RandomOptimizer, which optimize a cost function over action sequences.
• We provide a state based cost function. To get the cost of a trajectory, you need to sum/average over the cost of all the states.
• Sometimes the cost function can give nan values. Make sure to deal with such cases in your code.