A comprehensive collection of Reinforcement Learning algorithm examples, designed for both research and educational purposes.
- gym: latest version
- python: 3.9
For any algorithm example you choose to run, like DQN.py, execution will result in the creation of both a TensorBoard file and a model file. To visualize the progress and performance of the Reinforcement Learning algorithm:
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Execute your desired RL algorithm:
python DQN.py
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Launch TensorBoard using the generated log file:
tensorboard --logdir=[path_to_your_tensorboard_file] --port=6006
This allows you to track and visualize the algorithm's improvement over time.