Using Unity ML-Agents. link to banana collector!

For this project, agent will be trained to navigate and collect bananas in a sqare world.

Reward as follows: A reward of +1 is provided for collecting a yellow banana, and a reward of -1 is provided for collecting a blue banana.

Objective of agent:

Thus, the goal of your agent is to collect as many yellow bananas as possible while avoiding blue bananas.

State space in the environment:

Number of state space : 37
Vector of state space type : continuous
The state space has 37 dimensions and contains the agent's velocity, along with ray-based perception of objects around the agent's forward direction. 

State space : Number of state space : 37

[ 1.          0.          0.          0.          0.84408134  0.          0.    1.          0.          0.0748472   0.          1.          0.          0.]

Action space: Number of action space : 4

0 - move forward.
1 - move backward.
2 - turn left.
3 - turn right

Completion of successful agent:

The task is episodic, and in order to solve the environment, agent must get an average score of +13 over 100 consecutive episodes.

Deep Q-Network is a popular technique when coming to training agent to play famous games like Atari, however issue such as overestimation is a factor that could degrade the performance of agent using DQN alone.

Double DQN is able to reduce overestimation problem (overoptimism) in DQN using technique of target network as reference.

Reference : Minh et al. 2015 https://arxiv.org/pdf/1509.06461.pdf

Figure above show that highlighted green is the higher score of the game. DQN shown to be better in certain cases . In banana collector , benchmark will be conducted by implementing both DQN and DDQN to see the comparison of both algorithm.

• report.pdf: a document that describes the details of the different implementation, along with ideas for future work.
• jupyter notebook Banana_Navigator/Navigation.ipynb: Notebook to run agent learning at DQN and DDQN with the switch at agent class DDQN=False (DQN) and DDQN=True (DDQN).
• checkpoint_ddqn.pth : Weight file that has the trained agent using DDQN.
• checkpoint_dqn.pth : Weight file that has the trained agent using DQN.
• requiremets.txt : File for dependencies . Can run trhough pip install -r requirements.txt

1. Download the environment from one of the links below. You need only select the environment that matches your operating system:

   • Linux: click here
   • Mac OSX: click here
   • Windows (32-bit): click here
   • Windows (64-bit): click here

   (For Windows users) Check out this link if you need help with determining if your computer is running a 32-bit version or 64-bit version of the Windows operating system.

   (For AWS) If you'd like to train the agent on AWS (and have not enabled a virtual screen), then please use this link to obtain the environment.

2. Place the file in the DRLND GitHub repository, in the p1_navigation/ folder, and unzip (or decompress) the file.

Follow the instructions in Banana_Navigator/Navigation.ipynb to get started with training your own agent!

Deep Q-network rolling standard deviation and rolling mean reward graph

Double Deep Q-network rolling standard deviation and rolling mean reward graph

DQN vs DDQN stability in rewards over 1500 episodes