cartpole_q_learning_doc2.md

cartpole-q-learning

Solve CartPole environment on OpenAI Gym by simple q-learning algorithm (Parameter tuned version)

• gamma: 0.99
• bin size: [3, 3, 8, 5]
• low bound: [None, -0.5, None, -math.radians(50)]
• high bound: [None, 0.5, None, math.radians(50)]
• learning rate update rule: max(0.1, min(0.5, 1.0 - math.log10((t + 1) / 25)))
• epsilon update rule: max(0.01, min(1.0, 1.0 - math.log10((t + 1) / 25)))

These parameter settings are refered to sakulkar's algorithm.
You can confim no parameter tuning version at here. You can feel the effect of these.

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Model Overview