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atari-games

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This repository hosts Jupyter notebooks showcasing the training of Atari games using a variety of Deep Reinforcement Learning (RL) algorithms such as Proximal Policy Optimization (PPO), Deep Deterministic Policy Gradient (DDPG), Deep Q-Networks (DQN), Advantage Actor-Critic (A2C), and more.

  • Updated Jun 10, 2024
  • Jupyter Notebook

In this project, we attempt to equip the agent with the recognition of basic components of an Atari game environment through curriculum learning—gleaned from human developmental psychology—and evaluate its performance. Our best agent was pre-trained on a carefully designed curriculum to learn to complete a new game 5x faster than regular agents.…

  • Updated Aug 10, 2020
  • Python

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