LwH | Learning with Helper | Reinforcement Learning library

LwH is a Python library typically used in Artificial Intelligence, Reinforcement Learning, Deep Learning, Pytorch, Tensorflow applications. LwH has no bugs, it has no vulnerabilities and it has low support. However LwH build file is not available. You can download it from GitHub.

This repository includes codes for the algorithm Deep Reinforcement Learning with Non-Expert Helper (LwH) proposed in the paper titled "Deep Reinforcement Learning-based Autonomous UAV Navigation with Sparse Rewards". The codes are based on []. Before running the code, you need to install the environment 'uav-v0' (which can be found at) or other gym environments with continuous action psaces and low-dimensional observation space (e.g., mujoco envs). To run the code, simply execute: python main.py --env=uav-v0 --variance=0.4 --demo-type=uav --use-prior. where 'variance' specifies the initial variance of the prior policy, 'demo-type' specifies the prior policy type (uav->SenAvo-Pri; uav_wrong->Naive-Pri). To evaluate the learned policy, simply execute: python gym_eval.py --env=uav-v0 --variance=0 --load-model-dir=path-to-the-model.