Skip to content

Pratical Reinforcement Learning Course Project (Atari-Breakout)

Notifications You must be signed in to change notification settings

Sykarius/PRL-Project

Repository files navigation

PRL-Project

Pratical Reinforcement Learning Course Project (Atari-Breakout)

Table Of Contents:

Directory Structure:

.
|
|   |-- frames(the data)
|   |    |-- stack
|   |    |-- nostack
|   |-- logs
|   |    |-- training.log
|   |-- agent
|   |    |-- model1
|   |        |-- videos(all test videos)
|   |    |-- model2
|   |        |-- videos(all test videos)
|   |    |-- model3
|   |        |-- videos(all test videos)
|   |-- saved
|   |    |-- model1
|   |        |-- checkpoints(from training)
|   |    |-- model2
|   |        |-- checkpoints(from training)
|   |    |-- model3
|   |        |-- checkpoints(from training)
|    datasets.py
|    gendata_nostack.py
|    gendata_stack.py
|    models.py
|    README.md 
|    test_nostack.py
|    test_stack.py
|    train_nostack.py
|    train_stack.py

Steps to Run Tests:

    • For Model 1 & Model 2:
      python test_stack.py --name=[model1|model2] --max_steps=N (max number of steps in one episode) --ckpt=ckpt_name (only name of the checkpoint not the path, without '.pt' extension)
      
      For example:
      python test_stack.py --name=model2 --max_steps=30000 --ckpt=ckpt_8
      
    • Model 3:
      python test_nostack.py --max_steps=N (max number of steps in one episode) --ckpt=ckpt_name (only name of the checkpoint not the path, without '.pt' extension)
      
      For example:
      python test_nostack.py --max_steps=30000 --ckpt=ckpt_5
      
  1. The output of the above statement will be present in the agent folder under the corresponding model's folder.(The highest test_n will be the latest run)

Training Procedure:

  • Model 1 & Model 2:

    python gendata_stack.py
    

    this is to play the game and generate data. Also make sure 'frames/stack' is empty.

    python train_stack.py --name=[model1|model2] --ckpt=ckpt_name --epochs=N --batchs=M --learning_rate=float
    
  • Model 3:

    python gendata_nostack.py
    

    this is to play the game and generate data. Also make sure 'frames/nostack' is empty.

    python train_nostack.py --ckpt=ckpt_name --epochs=N --batchs=M --learning_rate=float
    

models.py contains all the required model definitions.
datasets.py contains all the required pre-processing logic.

Checkpoint Location:

The checkpoints are stored in the saved directory within the corresponding model folder.

About

Pratical Reinforcement Learning Course Project (Atari-Breakout)

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages