Super Reinforcement Bros: Playing Super Mario Bros with Reinforcement Learning
We plan to apply and adjust some well-known reinforcement learning (RL) algorithms to train an automatic agent to play the 1985 Nintendo game Super Mario Bros under a speedrun rule. The agent may learn several control policies from raw pixel data by using deep reinforcement learning. By the end of the project, we expect the model to perform comparably to or top human players in a given stage. Code and pre-trained models are available at https://github.com/AnthonySong98/Super-Mario-Bros-PPO. The video recording is available at https://drive.google.com/file/d/1A9d81LUKM9h8Msw9l9Xu2PrpCXnZGgcl/view.
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