Bluedeck Library 圖書館卡
Bluedeck Library 圖書館卡
存檔UTC時間 2022年6月24日 04:26 存檔編者 Kcx36 當前版本號 72322439
{{vfd|沒有足夠的可靠資料來源能夠讓這個條目符合Wikipedia:關注度 中的標準|date=2022/06/24}}
{{Notability|time=2022-05-25T01:09:01+00:00}}
帶有經驗回放的演員-評論家算法 （{{lang-en|Actor-Critic with Experience Replay}}），簡稱ACER 。是2017年由DeepMind 團隊在提出的算法。其論文發表在{{le|ICLR|International Conference on Learning Representations}}上。該文提出了一種基於深度強化學習Actor-Critic下帶有經驗回放 的算法，能夠在變化的環境中取得不錯的效果，其中包括了57個Atari遊戲以及一些需要持續控制的問題。[ 1]
在強化學習 中，環境交互需要付出極大的代價；這與普通的分類、回歸問題不同，會消耗大量的時間和系統資源。有效採樣（{{lang-en|Sample Efficiency}}）的方法可以使得算法在與環境交互較少的情況下獲得較好的結果。其中，為了提高有效採樣，使用經驗回放是一個很好的方法。而在強化學習中，如果採樣時所選取的策略{{lang-en|policy}}與選取動作時所用到的策略不同，我們將這種情況稱之為離軌策略（{{lang-en|off-policy}}）控制。
ACER就是一種離軌策略下的演員評論家算法（{{lang-en|off-policy actor-critic}}）。
對於離軌策略而言，我們採樣所得到的軌跡是與同軌策略（{{lang-en|on-policy}}）不同的。這裏同軌是指採樣時所用的策略與選取動作時的策略相同。所以需要用到重要性採樣來對其進行調整。加上重要性採樣的權重後策略梯度可以被寫作
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{\displaystyle {\hat {g}}=(\prod _{t=0}^{k}\rho _{t})\sum _{t=0}^{k}(\sum _{i=0}^{k-t}\gamma ^{i}r_{t+i})\nabla _{\theta }\log \pi _{\theta }(a_{t}|x_{t})}
據Off-Policy Actor-Critic稱，離線策略的策略梯度可以拆解為
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{\displaystyle g=\mathbb {E_{\beta }} [\rho _{t}\nabla _{\theta }(a_{t}|x_{t})Q^{\pi }(x_{t},a_{t})]}
[ 2]
由於重要性採樣的參數
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{\displaystyle \rho _{t}={\pi (a_{t}|x_{t}) \over \mu (a_{t}|x_{t})}}
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{\displaystyle \mathbb {E} _{\mu }[\rho _{t}\cdot \cdot \cdot ]=\mathbb {E} _{\mu }[{{\bar {\rho }}_{t}\cdot \cdot \cdot }]+\mathbb {E} _{a\sim \pi }[[{\rho _{t}(a)-c \over \rho _{t}}]+\cdot \cdot \cdot ]}
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{\displaystyle {\bar {\rho }}_{t}=min(c,\rho _{t})}
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{\displaystyle \mathbb {E} _{\mu }[{{\bar {\rho }}_{t}\cdot \cdot \cdot }]<c}
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{\displaystyle \mathbb {E} _{a\sim \pi }[[{\rho _{t}(a)-c \over \rho _{t}}]+\cdot \cdot \cdot ]<1}
動作值函數
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{\displaystyle Q^{\pi }(x_{t},a_{t})}
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{\displaystyle Q^{ret}(x_{t},a_{t})=r_{t}+\gamma {\bar {\rho _{t+1}}}[Q^{ret}(x_{t+1},a_{t+1})-Q(x_{t+1},a_{t+1})]+\gamma V(x_{t+1})}
以上的Q函數和V函數的估計使用了dueling network的結構。使用採樣的方法計算
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{\displaystyle {\tilde {Q}}_{\theta _{v}}(x_{t},a_{t})\sim V_{\theta _{v}}(x_{t})+A_{\theta _{v}}(x_{t},a_{t})-{1 \over n}\sum _{i=1}^{n}A_{\theta _{v}}(x_{t},u_{i}),and\ \ u_{i}\sim \pi _{\theta }(\cdot |x_{t})}
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{\displaystyle Q_{\theta _{v}}}
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{\displaystyle A_{\theta _{v}}}
綜合前三項，最終得到了ACER的離線策略梯度解析失败 (SVG（MathML可通过浏览器插件启用）：从服务器“http://localhost:6011/zh.wikipedia.org/v1/”返回无效的响应（“Math extension cannot connect to Restbase.”）：): {\displaystyle \widehat{g_t}^{acer} = \bar{\rho_t}\nabla _{\phi _\theta(x_t)}\log f(a_t|\phi_\theta(x))[Q^ret(x_t,a_t)- V_{\theta_v}(x_t)]+\mathbb{E}_{a\sim\pi}([{{((}}\rho_t(a)-c}\over{\rho_t(a)}}]_+\nabla_{\phi_\theta(x_t)} \log f(a_t|\phi_\theta(x))[Q_{\theta_v}(x_t,a)-V_{\theta_v}(x_t)]}
通過寫出信賴域最優化問題
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{\displaystyle minimize_{z}\ \ {1 \over 2}\left\Vert \ {\hat {g_{t}}}^{acer}-z\right\Vert _{2}^{2}}
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{\displaystyle subject\ \ to\ \ \nabla _{\phi _{\theta }(x_{t})D_{KL}}[f(\cdot |\phi _{\theta _{a}}(x_{t}))||f(\cdot |\phi _{\theta }(x_{t}))]^{T}z\leq \delta }
直接解析求得最優解解析失败 (SVG（MathML可通过浏览器插件启用）：从服务器“http://localhost:6011/zh.wikipedia.org/v1/”返回无效的响应（“Math extension cannot connect to Restbase.”）：): {\displaystyle z^* = \hat{g_t}^{acer}-max\{ 0,{{((}}k^T \hat{g_t}^{acer}-\delta}\over {||k||^2_2}} \}k}
得到參數更新公式解析失败 (SVG（MathML可通过浏览器插件启用）：从服务器“http://localhost:6011/zh.wikipedia.org/v1/”返回无效的响应（“Math extension cannot connect to Restbase.”）：): {\displaystyle \theta\leftarrow \theta +{{((}}\partial \phi_\theta(x)}\over{\partial\theta}}z^*}
算法1：對於離散動作情況下ACER的主算法
初始化全局共享參數向量
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{\displaystyle \theta \ \ and\ \ \theta _{v}}
設置回放率
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{\displaystyle r}
在達到最大迭代次數或者時間用完前：
調用算法2中的在線策略ACER
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{\displaystyle n\leftarrow \ \ Possion(r)}
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{\displaystyle i\in \{1,\cdot \cdot \cdot ,n\}}
調用算法2中的離線策略ACER 算法2：離散動作下的ACER
重置梯度
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{\displaystyle d\theta \leftarrow 0\ \ and\ \ d\theta _{v}\leftarrow 0}
初始化參數
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{\displaystyle \theta '\leftarrow \theta \ \ and\ \ \theta '_{v}\leftarrow \theta _{v}}
如果不是在線策略：
從經驗回放中採樣軌跡
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{\displaystyle \{x_{0},a_{0},r_{0},\mu (\cdot |x),\cdot \cdot \cdot ,x_{k},a_{k},r_{k},\mu (\cdot |x_{k})\}}
否則，獲取狀態
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{\displaystyle x_{0}}
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{\displaystyle i\in \{0,\cdot \cdot \cdot ,k\}}
計算
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{\displaystyle f(\cdot |\phi _{\theta '}(x_{i})),Q_{\theta '_{v}}(x_{i},\cdot )}
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{\displaystyle f(\cdot |\phi _{\theta _{a}}(x_{i}))}
如果是在線策略則
依據
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{\displaystyle f(\cdot |\phi '(x_{i}))}
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{\displaystyle a_{i}}
得到回報
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{\displaystyle r_{i}}
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{\displaystyle x_{i+1}}
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{\displaystyle \mu (\cdot |x_{i})\leftarrow f(\cdot |\phi _{\theta '}(x_{i}))}
解析失败 (语法错误): {\displaystyle \bar{\rho_i}\leftarrow min\{1,{{((}}f(a_i|\phi_{\theta'}(xi))}\over{\mu(a_i|x_i)}} \}}
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{\displaystyle Q^{ret}\leftarrow {\begin{cases}0\ \ for\ \ terminial\ \ x_{k}\\\sum _{a}Q_{\theta '_{v}}(x_{k},a)f(a|\phi _{\theta '}(x_{k}))\ \ otherwise\end{cases}}}
對於
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{\displaystyle i\in \{k-1,\cdot \cdot \cdot ,0\}}
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{\displaystyle Q^{ret}\leftarrow r_{i}+\gamma Q^{ret}}
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{\displaystyle V_{i}\leftarrow \sum _{a}Q_{\theta '_{v}}(x_{i},a)f(a|\phi _{\theta '}(x_{i}))}
計算信賴域更新所需的：
解析失败 (SVG（MathML可通过浏览器插件启用）：从服务器“http://localhost:6011/zh.wikipedia.org/v1/”返回无效的响应（“Math extension cannot connect to Restbase.”）：): {\displaystyle g \leftarrow min \{ c,\rho_i(a_i)\} \nabla_{\phi_'(x_i)}\log f(a_i|\phi_{\theta'}(x_i))(Q^{ret}-V_i)+ \sum_a[1-{{((}}c}\over{\rho_i(a)}}]_+ f(a|\phi_{\theta'}(x_i))\nabla_{\phi_{\theta'}(x_i)}\log f(a|\phi_{\theta'}(x_i))(Q_{\theta'_v}(x_i,a_i)-V_i)}
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{\displaystyle k\leftarrow \nabla _{\phi _{\theta '}(x_{i})}D_{KL}[f(\cdot |\phi _{\theta _{a}}(x_{i}))||f(\cdot |\phi _{\theta '}(x_{i})]}
累積梯度解析失败 (语法错误): {\displaystyle \theta':d\theta'\leftarrow +{{((}}\partial \phi_{\theta'}(x_i)}\over{\partial\theta'}}(g-max\{ 0,{{((}}k^Tg-\delta}\over{||k||^2_2}}k \})}
累積梯度
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{\displaystyle \theta '_{v}:d\theta _{v}+\nabla _{\theta '_{v}}(Q^{ret}-Q_{\theta '_{v}}(x_{i},a_{i}))+V_{i}}
用
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{\displaystyle d\theta ,d\theta _{v}}
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{\displaystyle \theta ,\theta _{v}}
更新平均策略網絡：
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{\displaystyle \theta _{a}\leftarrow \alpha \theta _{a}+(1-\alpha )\theta }
![{\displaystyle g=\mathbb {E_{\beta }} [\rho _{t}\nabla _{\theta }(a_{t}|x_{t})Q^{\pi }(x_{t},a_{t})]}](https://wikimedia.org/api/rest_v1/media/math/render/svg/2360ee0a1c43f5e12f32bf393a6feb6cb2f86221)
![{\displaystyle \mathbb {E} _{\mu }[\rho _{t}\cdot \cdot \cdot ]=\mathbb {E} _{\mu }[{{\bar {\rho }}_{t}\cdot \cdot \cdot }]+\mathbb {E} _{a\sim \pi }[[{\rho _{t}(a)-c \over \rho _{t}}]+\cdot \cdot \cdot ]}](https://wikimedia.org/api/rest_v1/media/math/render/svg/40a23ec24e3a24812baa0530c8e038dd393bcfee)
![{\displaystyle \mathbb {E} _{\mu }[{{\bar {\rho }}_{t}\cdot \cdot \cdot }]<c}](https://wikimedia.org/api/rest_v1/media/math/render/svg/0ea0f22d5c7eb16b283a2bf181d3959843262f40)
![{\displaystyle \mathbb {E} _{a\sim \pi }[[{\rho _{t}(a)-c \over \rho _{t}}]+\cdot \cdot \cdot ]<1}](https://wikimedia.org/api/rest_v1/media/math/render/svg/aa3d5ed575ec763a9ee144e59a8ae3ab460925e7)
![{\displaystyle Q^{ret}(x_{t},a_{t})=r_{t}+\gamma {\bar {\rho _{t+1}}}[Q^{ret}(x_{t+1},a_{t+1})-Q(x_{t+1},a_{t+1})]+\gamma V(x_{t+1})}](https://wikimedia.org/api/rest_v1/media/math/render/svg/01b71c97c8b395ceecb607ccb50eca09b3e77bcd)
![{\displaystyle subject\ \ to\ \ \nabla _{\phi _{\theta }(x_{t})D_{KL}}[f(\cdot |\phi _{\theta _{a}}(x_{t}))||f(\cdot |\phi _{\theta }(x_{t}))]^{T}z\leq \delta }](https://wikimedia.org/api/rest_v1/media/math/render/svg/622509e52be983ce5af4effa9e611ac46396f207)
![{\displaystyle k\leftarrow \nabla _{\phi _{\theta '}(x_{i})}D_{KL}[f(\cdot |\phi _{\theta _{a}}(x_{i}))||f(\cdot |\phi _{\theta '}(x_{i})]}](https://wikimedia.org/api/rest_v1/media/math/render/svg/6efb04f1e78599f148791badf9502d0bca7064d6)
