Reinforcement learning and deep learning are both subsets of machine learning.
The main differences between reinforcement learning and deep learning are:
- Deep learning uses labeled training samples, while reinforcement learning does not use labels. Instead, it learns through rewards and punishments provided by the environment.
- The learning process in deep learning is static, while in reinforcement learning it is dynamic. The difference between static and dynamic lies in whether there is interaction with the environment. Deep learning learns from specific samples, while reinforcement learning interacts with the environment and learns from the rewards and punishments provided by the environment.
- Deep learning is mainly used to solve perception problems, while reinforcement learning is mainly used to solve decision-making problems. Therefore, deep learning is more like the senses, while reinforcement learning is more like the brain.
ML-Agents is an open-source Unity plugin that allows us to train intelligent agents in game and simulation environments.
- Download ML-Agents, do not ask me why I'm not using version 20.
Link to download ML-Agents - Download Conda (Python environment).
Link to download Conda - Create a Conda environment.
conda create -n base1 python=3.8
- Switch to the created environment.
conda activate base1
- Install ML-Agents.
cd C:\Users\546546\Desktop\ml-agents-release_19\ml-agents-release_19 pip install torch~=1.7.1 pip install -e ./ml-agents-envs pip install -e ./ml-agents pip uninstall protobuf pip install protobuf==3.19.6 pip uninstall numpy pip install numpy==1.19
- After completing the development of the game logic, start training.
mlagents-learn config/ppo/ball.yaml --run-id=t1
For detailed tutorials, you can watch my YouTube tutorial.
Link to YouTube tutorial