Obstacle Tower Challenge using Deep Reinforcement Learning

Unity Obstacle Tower is a challenging realistic 3D, third person perspective and procedurally generated environment, which we use as the benchmark to test the performance of our deep RL models.

Proximal Policy Optimization (PPO) and Advantage Actor Critic (A2C), both combined with Intrinsic Curiosity Model (ICM), are implemented. We train our agent using both PPO and A2C while calculating several metrics such as loss function and reward to evaluate these two methods.

More details regarding this project can be viewed in our paper.


Requirements: see requirements.txt. We also require python 3.5+ and pip.

Install dependencies with pip install -r requirements.txt.

Download the environment at Obstacle Tower Github page here, unzip it and place it into the root of the project.

Training an agent


Run python3 -m agent.learn. This will initialize training with default parameters and it will use A2C as a learning method.

Run python3 -m agent.learn -h to check all options and their description.


Training can be visualized with TensorboardX.
After initial collection of observation stops and training starts, a log file that recording the data during the training process will be created in the runs/ file.

Run tensorboard --logdir runs/<file_name>. This will start server on localhost:6006 by default.

For example, you can run tensorboard --logdir runs/a2c to see the visualization results of a training process using the given a2c model.

Here is a screenshot of the visualization example:

Running the agent

Run python3 -m runner --model_name <model_name> This will start inference mode with selected model.

Two pretrained sample models are placed in models/ directory.
To check all available options run python3 -m runner -h.
Agent runs until in-game time runs up.

For example, you can run python -m runner --model_name model_a2c_750.bin using the given sample model.

Here is a screenshot of a single run:


View Github