MahanVeisi8/RL_practices
Collection of my Reinforcement Learning (RL) practices including DQN, D3QN, and Adaptive Gamma, applied to the Lunar Lander and CartPole environments. đđšī¸
This collection helps you understand how different reinforcement learning (RL) algorithms work by applying them to classic control problems like landing a spaceship or balancing a pole. You'll put in the problem setup and get out trained models and visualizations showing how well each algorithm learns to solve the task. This is ideal for students, researchers, or practitioners learning about or experimenting with RL algorithms.
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Use this if you want to explore, compare, and visualize the performance of various reinforcement learning algorithms on well-known, foundational control tasks.
Not ideal if you need to apply reinforcement learning to a complex, real-world custom environment or are looking for pre-trained models for immediate deployment.
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Oct 21, 2024
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