{ "

Generalized Advantage Estimation (GAE)

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This is a PyTorch implementation of paper Generalized Advantage Estimation.

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You can find an experiment that uses it here.

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\u5e7f\u4e49\u4f18\u52bf\u4f30\u8ba1 (GAE)

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\u8fd9\u662f\u8bba\u6587\u5e7f\u4e49\u4f18\u52bf\u4f30\u8ba1\u7684 PyTorch \u5b9e\u73b0\u3002

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\u4f60\u53ef\u4ee5\u5728\u8fd9\u91cc\u627e\u5230\u4e00\u4e2a\u4f7f\u7528\u5b83\u7684\u5b9e\u9a8c\u3002

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Calculate advantages

\n_^_0_^_

_^_1_^_ is high bias, low variance, whilst _^_2_^_ is unbiased, high variance.

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We take a weighted average of _^_3_^_ to balance bias and variance. This is called Generalized Advantage Estimation. _^_4_^_ We set _^_5_^_, this gives clean calculation for _^_6_^_

\n_^_7_^_": "

\u8ba1\u7b97\u4f18\u52bf

\n_^_0_^_

_^_1_^_\u662f\u9ad8\u504f\u5dee\uff0c\u4f4e\u65b9\u5dee\uff0c\u800c_^_2_^_\u65e0\u504f\u5dee\uff0c\u9ad8\u65b9\u5dee\u3002

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\u6211\u4eec\u91c7\u7528\u52a0\u6743\u5e73\u5747\u503c_^_3_^_\u6765\u5e73\u8861\u504f\u5dee\u548c\u65b9\u5dee\u3002\u8fd9\u79f0\u4e3a\u5e7f\u4e49\u4f18\u52bf\u4f30\u8ba1\u3002_^_4_^_\u6211\u4eec\u8bbe\u7f6e_^_5_^_\uff0c\u8fd9\u7ed9\u51fa\u4e86\u5e72\u51c0\u7684\u8ba1\u7b97_^_6_^_

\n_^_7_^_", "

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_^_0_^_

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_^_0_^_

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advantages table

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\u4f18\u52bf\u8868

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mask if episode completed after step _^_0_^_

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\u5982\u679c\u5267\u96c6\u5728\u6b65\u9aa4\u4e4b\u540e\u5b8c\u6210\uff0c\u8bf7\u63a9\u76d6_^_0_^_

\n", "A PyTorch implementation/tutorial of Generalized Advantage Estimation (GAE).": "\u5e7f\u4e49\u4f18\u52bf\u4f30\u8ba1\uff08GAE\uff09\u7684 PyTorch \u5b9e\u73b0/\u6559\u7a0b\u3002", "Generalized Advantage Estimation (GAE)": "\u5e7f\u4e49\u4f18\u52bf\u4f30\u8ba1 (GAE)" }