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rl-agents

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This project focuses on comparing different Reinforcement Learning Algorithms, including monte-carlo, q-learning, lambda q-learning epsilon-greedy variations, etc.

  • Updated Feb 15, 2022
  • Python

Our project focuses on the problem of generating synthetic levels of a game such that the levels can be used to learn an optimal policy for playing the game. Given a few pre-existing game levels we want to use deep generative models (like GANs) to generate new additional game levels. We will then train an RL agent on these levels to learn a gene…

  • Updated Dec 14, 2020
  • Python

This project trains and evaluates a Proximal Policy Optimization (PPO) agent to play the Atari game Atlantis using Stable Baselines3. The agent is trained with a Convolutional Neural Network (CNN) policy and evaluated for its performance in the game. It includes scripts for training, evaluating, and real-time gameplay rendering.

  • Updated Sep 13, 2024
  • Jupyter Notebook

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