CartPole using RL
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In this example we implement a simple Deep Reinforcement Learning solution for the classic CartPole problem. We implement a custom reward function and then we use three different strategies to strike a balance between exploration and exploitation and use DQN to approximate Q-values: - Epsilon greedy exploration - Noisy Networks - Thompson Sampling
Tasks: Reinforcement Learning
Task Categories: Reinforcement Learning
Published: 03/29/23
Tags
Reinforcement Learning
Epsilon Greedy
Noisy Network
Thompson Sampling
CartPole
pytorch
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