Behavior Cloning (BC) and Behavior Cloning from Observation (BCO)

• Implementation for Behavior Cloning (BC) and behavior cloning from observation (BCO) (pdf) in Pytorch for OpenAI Gym Environment

• Behavior Cloning (BC) and behavior cloning from observation (BCO) are Imitation Learning algorithms

• Behavior Cloning (BC) assume that you have access to expert's states and actions but behavior cloning from observation assume that you have access to expert's States only

How it works?

1- Collecting data:

• Learner: exploration policy, save states and actions

• Expert: train expert (if you don’t have one), save states only.

• all data available here

2- Train Inverse dynamic model (T):

• Input: Learner current state and Learner next state.

• Output: predicted Learner current action.

• Loss function: MSE, L1loss or NLL (predicted Learner current action, Learner current action).

3- Test: Inverse dynamic model (T):

• Input: Expert current state and Expert next state

• Output: predicted Expert current action.

4- Train Behaviour model (policy):

• Input: Expert current state.

• Output: prediction of predicted Expert current action.

• Loss: MSE, L1loss or NLL (prediction of predicted Expert current action, predicted Expert current action).

5- Learner interacts with environment BCO(alpha):

• Learner use Behaviour model (policy) to get action given current state.

• Collect new data (states and actions)

• Use collected data to update Inverse dynamic model (T) and Behaviour model (policy) (repeat 2, 3, and 4)

OpenAI Gym Enviroment

• Open AI Gym has several environments, We Use classical control environments Pendulum and Bipedal Walker2D environmens.

Installing

pip install gym
pip install numpy
pip install box2d-py
pip install torchvision

Data

• Pendulum and [Bipedal Walker2D] (https://github.com/openai/gym/wiki/BipedalWalker-v2) Exploration States and actions, also expert states you can download it from here

Results

Demo