The solution for the LunarLander-v2 gym environment.
The code is based on materials from Udacity Deep Reinforcement Learning Nanodegree Program.
The interaction with the environment is based on the following four discrete actions:
- do nothing
- fire left orientation engine
- fire main engine
- fire right orientation engine.
The environment returns the state vector, where the first two comprises coordinates. The episode finishes if the lander crashes or comes to rest. LunarLander-v2 defines "solving" as getting an average reward of 200 over 100 consecutive trials. (https://github.com/openai/gym/wiki/Leaderboard) The environment is solved by using Dueling Double DQN algorithm, where actions selection based on epsilon-greedy policy.
In order to train the model or inference the computed weights, the following need to be installed:
- pytorch
- pybox2d
- gym
Since the repository provides the jupyter notebook, follow the steps of execution.