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--- |
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tags: |
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- Pixelcopter-PLE-v0 |
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- reinforce |
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- reinforcement-learning |
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- custom-implementation |
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- deep-rl-class |
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model-index: |
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- name: policy_grad_2-Pixelcopter-PLE-v0 |
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results: |
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- task: |
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type: reinforcement-learning |
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name: reinforcement-learning |
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dataset: |
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name: Pixelcopter-PLE-v0 |
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type: Pixelcopter-PLE-v0 |
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metrics: |
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- type: mean_reward |
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value: 70.30 +/- 33.94 |
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name: mean_reward |
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verified: false |
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--- |
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# **Reinforce** Agent playing **Pixelcopter-PLE-v0** |
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This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . |
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To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction |
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# Some math about 'Pixelcopter' training. |
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The game is to fly in a passage and avoid blocks. Let we have trained our agent so that the probability to crash at block is _p_ (low enogh, I hope). |
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The probability that the copter crashes exactly at _n_-th block is product of probabilities it doesn't crash at previous _(n-1)_ blocks and probability it crashes at current block: |
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$$P = {p \cdot (1-p)^{n-1}}$$ |
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The mathematical expectation of number of the block it crashes at is: |
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$$<n> = \sum_{n=1}^\infty{n \cdot p \cdot (1-p)^{n-1}} = \frac{1}{p}$$ |
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The std is: |
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$$std(n) = \sqrt{<n^2>-<n>^2}$$ |
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$$<n^2> = \sum_{n=1}^\infty{n^2 \cdot p \cdot (1-p)^{n-1}} = \frac{2-p}{p^2}$$ |
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$$std(n) = \sqrt{\frac{2-p}{p^2}-\left( \frac{1}{p} \right) ^2} = \frac{\sqrt{1-p}}{p}$$ |
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So difference is: |
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$$<n> - std(n) = \frac{1 - \sqrt{1-p}}{p}$$ |
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As long as |
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$$ 0 \le p \le 1 $$ |
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the following is true: |
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$$\sqrt{1-p} \ge 1-p$$ |
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$$<n> - std(n) = \frac{1 - \sqrt{1-p}}{p} \le \frac{1 - (1-p)}{p} = 1$$ |
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The scores _s_ in 'Pixelcopter' are the number of blocks passed decreased by 5 (for crash). So the average is lower by 5 and the std is the same. No matter how small _p_ is, our 'least score' is: |
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$$ (<n> - 5) - std(n) = <n> - std(n) - 5 \le - 4$$ |
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But as we use only 10 episodes to calculate statistics and episode duration is limited, we can still achieve the goal, better agent, more chances. But understanding this is disappointing |