mauicv / openai-gym-solns

Reinforcement Learning Solutions To Openai Gym Problems:

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Setup:

Create new virtual environment, activate and install requirements:

python3 -m venv venv
source venv/bin/activate
pip install --update pip
pip install -r requirements

Running Examples:

This repo uses click as a command line interface to reinforcment algorithms written on top of TensorFlow.

To see pretrained examples use python main.py example. The options are --target, --algorithm and --num_steps. target refers to the openai gym environment. Choices are cart-pole and luner-lander. Algorithm is the algorithm used to train the solution, choices are pg for Policy Gradient, dqn for Deep Q Network and ac for Actor Critic. --num_steps is just the number of iterations of the trained example solution we run.

Example:

python main.py example --target='luner-lander' --algorithm='pg'

Note:

• evo is experimental and I'm not sure it should be technically classed as an evolutionary algorithm.

TODO:

• Add policy delay to TD3