{"id":477698,"date":"2023-08-09T09:19:05","date_gmt":"2023-08-09T09:19:05","guid":{"rendered":""},"modified":"2023-09-05T11:15:15","modified_gmt":"2023-09-05T11:15:15","slug":"inverse-reinforcement-learning","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/vn\/wiki\/inverse-reinforcement-learning\/","title":{"rendered":"H\u1ecdc t\u0103ng c\u01b0\u1eddng ngh\u1ecbch \u0111\u1ea3o"},"content":{"rendered":"<p>H\u1ecdc t\u0103ng c\u01b0\u1eddng ngh\u1ecbch \u0111\u1ea3o (IRL) l\u00e0 m\u1ed9t tr\u01b0\u1eddng con c\u1ee7a h\u1ecdc m\u00e1y v\u00e0 tr\u00ed tu\u1ec7 nh\u00e2n t\u1ea1o t\u1eadp trung v\u00e0o vi\u1ec7c t\u00ecm hi\u1ec3u c\u00e1c ph\u1ea7n th\u01b0\u1edfng ho\u1eb7c m\u1ee5c ti\u00eau c\u01a1 b\u1ea3n c\u1ee7a m\u1ed9t t\u00e1c nh\u00e2n b\u1eb1ng c\u00e1ch quan s\u00e1t h\u00e0nh vi c\u1ee7a n\u00f3 trong m\u1ed9t m\u00f4i tr\u01b0\u1eddng nh\u1ea5t \u0111\u1ecbnh. Trong h\u1ecdc t\u0103ng c\u01b0\u1eddng truy\u1ec1n th\u1ed1ng, m\u1ed9t t\u00e1c nh\u00e2n h\u1ecdc c\u00e1ch t\u1ed1i \u0111a h\u00f3a ph\u1ea7n th\u01b0\u1edfng d\u1ef1a tr\u00ean ch\u1ee9c n\u0103ng ph\u1ea7n th\u01b0\u1edfng \u0111\u01b0\u1ee3c x\u00e1c \u0111\u1ecbnh tr\u01b0\u1edbc. Ng\u01b0\u1ee3c l\u1ea1i, IRL t\u00ecm c\u00e1ch suy ra ch\u1ee9c n\u0103ng khen th\u01b0\u1edfng t\u1eeb h\u00e0nh vi \u0111\u01b0\u1ee3c quan s\u00e1t, cung c\u1ea5p m\u1ed9t c\u00f4ng c\u1ee5 c\u00f3 gi\u00e1 tr\u1ecb \u0111\u1ec3 hi\u1ec3u qu\u00e1 tr\u00ecnh ra quy\u1ebft \u0111\u1ecbnh c\u1ee7a con ng\u01b0\u1eddi ho\u1eb7c chuy\u00ean gia.<\/p>\n<h2>L\u1ecbch s\u1eed ngu\u1ed3n g\u1ed1c c\u1ee7a h\u1ecdc t\u0103ng c\u01b0\u1eddng ngh\u1ecbch \u0111\u1ea3o v\u00e0 l\u1ea7n \u0111\u1ea7u ti\u00ean \u0111\u1ec1 c\u1eadp \u0111\u1ebfn n\u00f3<\/h2>\n<p>Kh\u00e1i ni\u1ec7m H\u1ecdc t\u0103ng c\u01b0\u1eddng ngh\u1ecbch \u0111\u1ea3o \u0111\u01b0\u1ee3c Andrew Ng v\u00e0 Stuart Russell gi\u1edbi thi\u1ec7u l\u1ea7n \u0111\u1ea7u ti\u00ean trong b\u00e0i b\u00e1o n\u0103m 2000 c\u1ee7a h\u1ecd c\u00f3 t\u1ef1a \u0111\u1ec1 \u201cThu\u1eadt to\u00e1n cho h\u1ecdc t\u0103ng c\u01b0\u1eddng ngh\u1ecbch \u0111\u1ea3o\u201d. B\u00e0i vi\u1ebft mang t\u00ednh \u0111\u1ed9t ph\u00e1 n\u00e0y \u0111\u00e3 \u0111\u1eb7t n\u1ec1n m\u00f3ng cho vi\u1ec7c nghi\u00ean c\u1ee9u IRL v\u00e0 c\u00e1c \u1ee9ng d\u1ee5ng c\u1ee7a n\u00f3 trong nhi\u1ec1u l\u0129nh v\u1ef1c kh\u00e1c nhau. K\u1ec3 t\u1eeb \u0111\u00f3, c\u00e1c nh\u00e0 nghi\u00ean c\u1ee9u v\u00e0 th\u1ef1c h\u00e0nh \u0111\u00e3 c\u00f3 nh\u1eefng b\u01b0\u1edbc ti\u1ebfn \u0111\u00e1ng k\u1ec3 trong vi\u1ec7c t\u00ecm hi\u1ec3u v\u00e0 c\u1ea3i ti\u1ebfn c\u00e1c thu\u1eadt to\u00e1n IRL, bi\u1ebfn n\u00f3 tr\u1edf th\u00e0nh m\u1ed9t k\u1ef9 thu\u1eadt thi\u1ebft y\u1ebfu trong nghi\u00ean c\u1ee9u tr\u00ed tu\u1ec7 nh\u00e2n t\u1ea1o hi\u1ec7n \u0111\u1ea1i.<\/p>\n<h2>Th\u00f4ng tin chi ti\u1ebft v\u1ec1 H\u1ecdc t\u0103ng c\u01b0\u1eddng ngh\u1ecbch \u0111\u1ea3o. M\u1edf r\u1ed9ng ch\u1ee7 \u0111\u1ec1 H\u1ecdc t\u0103ng c\u01b0\u1eddng ngh\u1ecbch \u0111\u1ea3o.<\/h2>\n<p>H\u1ecdc t\u0103ng c\u01b0\u1eddng ngh\u1ecbch \u0111\u1ea3o t\u00ecm c\u00e1ch gi\u1ea3i quy\u1ebft c\u00e2u h\u1ecfi c\u01a1 b\u1ea3n: \u201cPh\u1ea7n th\u01b0\u1edfng ho\u1eb7c m\u1ee5c ti\u00eau n\u00e0o m\u00e0 c\u00e1c t\u00e1c nh\u00e2n t\u1ed1i \u01b0u h\u00f3a khi \u0111\u01b0a ra quy\u1ebft \u0111\u1ecbnh trong m\u1ed9t m\u00f4i tr\u01b0\u1eddng c\u1ee5 th\u1ec3?\u201d C\u00e2u h\u1ecfi n\u00e0y r\u1ea5t quan tr\u1ecdng v\u00ec vi\u1ec7c hi\u1ec3u \u0111\u01b0\u1ee3c c\u00e1c ph\u1ea7n th\u01b0\u1edfng c\u01a1 b\u1ea3n c\u00f3 th\u1ec3 gi\u00fap c\u1ea3i thi\u1ec7n qu\u00e1 tr\u00ecnh ra quy\u1ebft \u0111\u1ecbnh, t\u1ea1o ra c\u00e1c h\u1ec7 th\u1ed1ng AI m\u1ea1nh m\u1ebd h\u01a1n v\u00e0 th\u1eadm ch\u00ed m\u00f4 h\u00ecnh h\u00f3a h\u00e0nh vi c\u1ee7a con ng\u01b0\u1eddi m\u1ed9t c\u00e1ch ch\u00ednh x\u00e1c.<\/p>\n<p>C\u00e1c b\u01b0\u1edbc ch\u00ednh li\u00ean quan \u0111\u1ebfn IRL nh\u01b0 sau:<\/p>\n<ol>\n<li>\n<p><strong>Quan s\u00e1t<\/strong>: B\u01b0\u1edbc \u0111\u1ea7u ti\u00ean trong IRL l\u00e0 quan s\u00e1t h\u00e0nh vi c\u1ee7a t\u00e1c nh\u00e2n trong m\u1ed9t m\u00f4i tr\u01b0\u1eddng nh\u1ea5t \u0111\u1ecbnh. Quan s\u00e1t n\u00e0y c\u00f3 th\u1ec3 \u1edf d\u1ea1ng tr\u00ecnh di\u1ec5n c\u1ee7a chuy\u00ean gia ho\u1eb7c d\u1eef li\u1ec7u \u0111\u01b0\u1ee3c ghi l\u1ea1i.<\/p>\n<\/li>\n<li>\n<p><strong>Ph\u1ee5c h\u1ed3i ch\u1ee9c n\u0103ng ph\u1ea7n th\u01b0\u1edfng<\/strong>: B\u1eb1ng c\u00e1ch s\u1eed d\u1ee5ng h\u00e0nh vi \u0111\u01b0\u1ee3c quan s\u00e1t, thu\u1eadt to\u00e1n IRL c\u1ed1 g\u1eafng kh\u00f4i ph\u1ee5c h\u00e0m khen th\u01b0\u1edfng gi\u1ea3i th\u00edch r\u00f5 nh\u1ea5t h\u00e0nh \u0111\u1ed9ng c\u1ee7a t\u00e1c nh\u00e2n. H\u00e0m khen th\u01b0\u1edfng \u0111\u01b0\u1ee3c suy ra ph\u1ea3i nh\u1ea5t qu\u00e1n v\u1edbi h\u00e0nh vi \u0111\u01b0\u1ee3c quan s\u00e1t.<\/p>\n<\/li>\n<li>\n<p><strong>T\u1ed1i \u01b0u h\u00f3a ch\u00ednh s\u00e1ch<\/strong>: Sau khi suy ra ch\u1ee9c n\u0103ng ph\u1ea7n th\u01b0\u1edfng, n\u00f3 c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng \u0111\u1ec3 t\u1ed1i \u01b0u h\u00f3a ch\u00ednh s\u00e1ch c\u1ee7a t\u00e1c nh\u00e2n th\u00f4ng qua c\u00e1c k\u1ef9 thu\u1eadt h\u1ecdc t\u0103ng c\u01b0\u1eddng truy\u1ec1n th\u1ed1ng. \u0110i\u1ec1u n\u00e0y d\u1eabn \u0111\u1ebfn qu\u00e1 tr\u00ecnh ra quy\u1ebft \u0111\u1ecbnh \u0111\u01b0\u1ee3c c\u1ea3i thi\u1ec7n cho \u0111\u1ea1i l\u00fd.<\/p>\n<\/li>\n<li>\n<p><strong>C\u00e1c \u1ee9ng d\u1ee5ng<\/strong>: IRL \u0111\u00e3 t\u00ecm th\u1ea5y c\u00e1c \u1ee9ng d\u1ee5ng trong nhi\u1ec1u l\u0129nh v\u1ef1c kh\u00e1c nhau, bao g\u1ed3m robot, xe t\u1ef1 h\u00e0nh, h\u1ec7 th\u1ed1ng khuy\u1ebfn ngh\u1ecb v\u00e0 t\u01b0\u01a1ng t\u00e1c gi\u1eefa ng\u01b0\u1eddi v\u00e0 robot. N\u00f3 cho ph\u00e9p ch\u00fang t\u00f4i l\u1eadp m\u00f4 h\u00ecnh v\u00e0 hi\u1ec3u h\u00e0nh vi c\u1ee7a chuy\u00ean gia, \u0111\u1ed3ng th\u1eddi s\u1eed d\u1ee5ng ki\u1ebfn th\u1ee9c \u0111\u00f3 \u0111\u1ec3 \u0111\u00e0o t\u1ea1o c\u00e1c t\u00e1c nh\u00e2n kh\u00e1c hi\u1ec7u qu\u1ea3 h\u01a1n.<\/p>\n<\/li>\n<\/ol>\n<h2>C\u1ea5u tr\u00fac b\u00ean trong c\u1ee7a h\u1ecdc t\u0103ng c\u01b0\u1eddng ngh\u1ecbch \u0111\u1ea3o. H\u1ecdc t\u0103ng c\u01b0\u1eddng ngh\u1ecbch \u0111\u1ea3o ho\u1ea1t \u0111\u1ed9ng nh\u01b0 th\u1ebf n\u00e0o.<\/h2>\n<p>H\u1ecdc t\u0103ng c\u01b0\u1eddng ngh\u1ecbch \u0111\u1ea3o th\u01b0\u1eddng bao g\u1ed3m c\u00e1c th\u00e0nh ph\u1ea7n sau:<\/p>\n<ol>\n<li>\n<p><strong>M\u00f4i tr\u01b0\u1eddng<\/strong>: M\u00f4i tr\u01b0\u1eddng l\u00e0 b\u1ed1i c\u1ea3nh ho\u1eb7c b\u1ed1i c\u1ea3nh trong \u0111\u00f3 t\u00e1c nh\u00e2n ho\u1ea1t \u0111\u1ed9ng. N\u00f3 cung c\u1ea5p cho t\u00e1c nh\u00e2n c\u00e1c tr\u1ea1ng th\u00e1i, h\u00e0nh \u0111\u1ed9ng v\u00e0 ph\u1ea7n th\u01b0\u1edfng d\u1ef1a tr\u00ean h\u00e0nh \u0111\u1ed9ng c\u1ee7a n\u00f3.<\/p>\n<\/li>\n<li>\n<p><strong>\u0110\u1ea1i l\u00fd<\/strong>: T\u00e1c nh\u00e2n l\u00e0 th\u1ef1c th\u1ec3 c\u00f3 h\u00e0nh vi m\u00e0 ch\u00fang ta mu\u1ed1n hi\u1ec3u ho\u1eb7c c\u1ea3i thi\u1ec7n. N\u00f3 th\u1ef1c hi\u1ec7n c\u00e1c h\u00e0nh \u0111\u1ed9ng trong m\u00f4i tr\u01b0\u1eddng \u0111\u1ec3 \u0111\u1ea1t \u0111\u01b0\u1ee3c c\u00e1c m\u1ee5c ti\u00eau nh\u1ea5t \u0111\u1ecbnh.<\/p>\n<\/li>\n<li>\n<p><strong>Tr\u00ecnh di\u1ec5n chuy\u00ean m\u00f4n<\/strong>: \u0110\u00e2y l\u00e0 nh\u1eefng minh ch\u1ee9ng v\u1ec1 h\u00e0nh vi c\u1ee7a chuy\u00ean gia trong m\u00f4i tr\u01b0\u1eddng nh\u1ea5t \u0111\u1ecbnh. Thu\u1eadt to\u00e1n IRL s\u1eed d\u1ee5ng c\u00e1c minh h\u1ecda n\u00e0y \u0111\u1ec3 suy ra h\u00e0m ph\u1ea7n th\u01b0\u1edfng c\u01a1 b\u1ea3n.<\/p>\n<\/li>\n<li>\n<p><strong>Ch\u1ee9c n\u0103ng khen th\u01b0\u1edfng<\/strong>: H\u00e0m ph\u1ea7n th\u01b0\u1edfng \u00e1nh x\u1ea1 c\u00e1c tr\u1ea1ng th\u00e1i v\u00e0 h\u00e0nh \u0111\u1ed9ng trong m\u00f4i tr\u01b0\u1eddng th\u00e0nh m\u1ed9t gi\u00e1 tr\u1ecb s\u1ed1, th\u1ec3 hi\u1ec7n m\u1ee9c \u0111\u1ed9 mong mu\u1ed1n c\u1ee7a c\u00e1c tr\u1ea1ng th\u00e1i v\u00e0 h\u00e0nh \u0111\u1ed9ng \u0111\u00f3. \u0110\u00e2y l\u00e0 kh\u00e1i ni\u1ec7m then ch\u1ed1t trong h\u1ecdc t\u0103ng c\u01b0\u1eddng v\u00e0 trong IRL, n\u00f3 c\u1ea7n \u0111\u01b0\u1ee3c suy ra.<\/p>\n<\/li>\n<li>\n<p><strong>Thu\u1eadt to\u00e1n h\u1ecdc t\u0103ng c\u01b0\u1eddng ngh\u1ecbch \u0111\u1ea3o<\/strong>: C\u00e1c thu\u1eadt to\u00e1n n\u00e0y l\u1ea5y s\u1ef1 minh h\u1ecda c\u1ee7a chuy\u00ean gia v\u00e0 m\u00f4i tr\u01b0\u1eddng l\u00e0m \u0111\u1ea7u v\u00e0o v\u00e0 c\u1ed1 g\u1eafng kh\u00f4i ph\u1ee5c h\u00e0m ph\u1ea7n th\u01b0\u1edfng. Nhi\u1ec1u c\u00e1ch ti\u1ebfp c\u1eadn kh\u00e1c nhau, ch\u1eb3ng h\u1ea1n nh\u01b0 IRL entropy t\u1ed1i \u0111a v\u00e0 IRL Bayesian, \u0111\u00e3 \u0111\u01b0\u1ee3c \u0111\u1ec1 xu\u1ea5t trong nhi\u1ec1u n\u0103m.<\/p>\n<\/li>\n<li>\n<p><strong>T\u1ed1i \u01b0u h\u00f3a ch\u00ednh s\u00e1ch<\/strong>: Sau khi kh\u00f4i ph\u1ee5c ch\u1ee9c n\u0103ng khen th\u01b0\u1edfng, n\u00f3 c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng \u0111\u1ec3 t\u1ed1i \u01b0u h\u00f3a ch\u00ednh s\u00e1ch c\u1ee7a t\u00e1c nh\u00e2n th\u00f4ng qua c\u00e1c k\u1ef9 thu\u1eadt h\u1ecdc t\u0103ng c\u01b0\u1eddng nh\u01b0 Q-learning ho\u1eb7c gradient ch\u00ednh s\u00e1ch.<\/p>\n<\/li>\n<\/ol>\n<h2>Ph\u00e2n t\u00edch c\u00e1c t\u00ednh n\u0103ng ch\u00ednh c\u1ee7a h\u1ecdc t\u0103ng c\u01b0\u1eddng ngh\u1ecbch \u0111\u1ea3o.<\/h2>\n<p>H\u1ecdc t\u0103ng c\u01b0\u1eddng ngh\u1ecbch \u0111\u1ea3o cung c\u1ea5p m\u1ed9t s\u1ed1 t\u00ednh n\u0103ng v\u00e0 l\u1ee3i th\u1ebf ch\u00ednh so v\u1edbi h\u1ecdc t\u0103ng c\u01b0\u1eddng truy\u1ec1n th\u1ed1ng:<\/p>\n<ol>\n<li>\n<p><strong>Ra quy\u1ebft \u0111\u1ecbnh gi\u1ed1ng con ng\u01b0\u1eddi<\/strong>: B\u1eb1ng c\u00e1ch suy ra ch\u1ee9c n\u0103ng khen th\u01b0\u1edfng t\u1eeb c\u00e1c minh ch\u1ee9ng c\u1ee7a chuy\u00ean gia v\u1ec1 con ng\u01b0\u1eddi, IRL cho ph\u00e9p c\u00e1c t\u00e1c nh\u00e2n \u0111\u01b0a ra quy\u1ebft \u0111\u1ecbnh ph\u00f9 h\u1ee3p h\u01a1n v\u1edbi s\u1edf th\u00edch v\u00e0 h\u00e0nh vi c\u1ee7a con ng\u01b0\u1eddi.<\/p>\n<\/li>\n<li>\n<p><strong>L\u1eadp m\u00f4 h\u00ecnh ph\u1ea7n th\u01b0\u1edfng kh\u00f4ng th\u1ec3 quan s\u00e1t \u0111\u01b0\u1ee3c<\/strong>: Trong nhi\u1ec1u t\u00ecnh hu\u1ed1ng th\u1ef1c t\u1ebf, ch\u1ee9c n\u0103ng khen th\u01b0\u1edfng kh\u00f4ng \u0111\u01b0\u1ee3c cung c\u1ea5p r\u00f5 r\u00e0ng, khi\u1ebfn vi\u1ec7c h\u1ecdc t\u0103ng c\u01b0\u1eddng truy\u1ec1n th\u1ed1ng tr\u1edf n\u00ean kh\u00f3 kh\u0103n. IRL c\u00f3 th\u1ec3 ph\u00e1t hi\u1ec7n ra nh\u1eefng ph\u1ea7n th\u01b0\u1edfng c\u01a1 b\u1ea3n m\u00e0 kh\u00f4ng c\u1ea7n s\u1ef1 gi\u00e1m s\u00e1t r\u00f5 r\u00e0ng.<\/p>\n<\/li>\n<li>\n<p><strong>T\u00ednh minh b\u1ea1ch v\u00e0 kh\u1ea3 n\u0103ng gi\u1ea3i th\u00edch<\/strong>: IRL cung c\u1ea5p c\u00e1c ch\u1ee9c n\u0103ng khen th\u01b0\u1edfng c\u00f3 th\u1ec3 gi\u1ea3i th\u00edch \u0111\u01b0\u1ee3c, cho ph\u00e9p hi\u1ec3u s\u00e2u h\u01a1n v\u1ec1 qu\u00e1 tr\u00ecnh ra quy\u1ebft \u0111\u1ecbnh c\u1ee7a c\u00e1c \u0111\u1ea1i l\u00fd.<\/p>\n<\/li>\n<li>\n<p><strong>Hi\u1ec7u qu\u1ea3 m\u1eabu<\/strong>: IRL th\u01b0\u1eddng c\u00f3 th\u1ec3 h\u1ecdc t\u1eeb s\u1ed1 l\u01b0\u1ee3ng minh h\u1ecda c\u1ee7a chuy\u00ean gia \u00edt h\u01a1n so v\u1edbi d\u1eef li\u1ec7u m\u1edf r\u1ed9ng c\u1ea7n thi\u1ebft cho vi\u1ec7c h\u1ecdc t\u0103ng c\u01b0\u1eddng.<\/p>\n<\/li>\n<li>\n<p><strong>Chuy\u1ec3n ti\u1ebfp h\u1ecdc t\u1eadp<\/strong>: Ch\u1ee9c n\u0103ng ph\u1ea7n th\u01b0\u1edfng \u0111\u01b0\u1ee3c suy ra t\u1eeb m\u1ed9t m\u00f4i tr\u01b0\u1eddng c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c chuy\u1ec3n sang m\u00f4i tr\u01b0\u1eddng t\u01b0\u01a1ng t\u1ef1 nh\u01b0ng h\u01a1i kh\u00e1c m\u1ed9t ch\u00fat, gi\u00fap gi\u1ea3m nhu c\u1ea7u h\u1ecdc l\u1ea1i t\u1eeb \u0111\u1ea7u.<\/p>\n<\/li>\n<li>\n<p><strong>X\u1eed l\u00fd ph\u1ea7n th\u01b0\u1edfng th\u01b0a th\u1edbt<\/strong>: IRL c\u00f3 th\u1ec3 gi\u1ea3i quy\u1ebft c\u00e1c v\u1ea5n \u0111\u1ec1 v\u1ec1 ph\u1ea7n th\u01b0\u1edfng th\u01b0a th\u1edbt, trong \u0111\u00f3 ph\u01b0\u01a1ng ph\u00e1p h\u1ecdc t\u0103ng c\u01b0\u1eddng truy\u1ec1n th\u1ed1ng g\u1eb7p kh\u00f3 kh\u0103n trong vi\u1ec7c h\u1ecdc do khan hi\u1ebfm ph\u1ea3n h\u1ed3i.<\/p>\n<\/li>\n<\/ol>\n<h2>C\u00e1c lo\u1ea1i h\u00ecnh h\u1ecdc t\u0103ng c\u01b0\u1eddng ngh\u1ecbch \u0111\u1ea3o<\/h2>\n<table>\n<thead>\n<tr>\n<th>Ki\u1ec3u<\/th>\n<th>S\u1ef1 mi\u00eau t\u1ea3<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>IRL Entropy t\u1ed1i \u0111a<\/td>\n<td>M\u1ed9t c\u00e1ch ti\u1ebfp c\u1eadn IRL gi\u00fap t\u1ed1i \u0111a h\u00f3a entropy trong ch\u00ednh s\u00e1ch c\u1ee7a \u0111\u1ea1i l\u00fd d\u1ef1a tr\u00ean ph\u1ea7n th\u01b0\u1edfng \u0111\u01b0\u1ee3c suy ra.<\/td>\n<\/tr>\n<tr>\n<td>IRL Bayes<\/td>\n<td>K\u1ebft h\u1ee3p m\u1ed9t khung x\u00e1c su\u1ea5t \u0111\u1ec3 suy ra s\u1ef1 ph\u00e2n b\u1ed5 c\u00e1c ch\u1ee9c n\u0103ng khen th\u01b0\u1edfng c\u00f3 th\u1ec3 c\u00f3.<\/td>\n<\/tr>\n<tr>\n<td>IRL \u0111\u1ed1i ngh\u1ecbch<\/td>\n<td>S\u1eed d\u1ee5ng c\u00e1ch ti\u1ebfp c\u1eadn l\u00fd thuy\u1ebft tr\u00f2 ch\u01a1i v\u1edbi b\u1ed9 ph\u00e2n bi\u1ec7t v\u00e0 b\u1ed9 t\u1ea1o \u0111\u1ec3 suy ra h\u00e0m ph\u1ea7n th\u01b0\u1edfng.<\/td>\n<\/tr>\n<tr>\n<td>H\u1ecdc ngh\u1ec1<\/td>\n<td>K\u1ebft h\u1ee3p IRL v\u00e0 h\u1ecdc t\u0103ng c\u01b0\u1eddng \u0111\u1ec3 h\u1ecdc h\u1ecfi t\u1eeb c\u00e1c cu\u1ed9c tr\u00ecnh di\u1ec5n c\u1ee7a chuy\u00ean gia.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>C\u00e1c c\u00e1ch s\u1eed d\u1ee5ng H\u1ecdc t\u0103ng c\u01b0\u1eddng ngh\u1ecbch \u0111\u1ea3o, c\u00e1c v\u1ea5n \u0111\u1ec1 v\u00e0 gi\u1ea3i ph\u00e1p li\u00ean quan \u0111\u1ebfn vi\u1ec7c s\u1eed d\u1ee5ng.<\/h2>\n<p>H\u1ecdc t\u0103ng c\u01b0\u1eddng ngh\u1ecbch \u0111\u1ea3o c\u00f3 nhi\u1ec1u \u1ee9ng d\u1ee5ng kh\u00e1c nhau v\u00e0 c\u00f3 th\u1ec3 gi\u1ea3i quy\u1ebft nh\u1eefng th\u00e1ch th\u1ee9c c\u1ee5 th\u1ec3:<\/p>\n<ol>\n<li>\n<p><strong>Ng\u01b0\u1eddi m\u00e1y<\/strong>: Trong l\u0129nh v\u1ef1c robot, IRL gi\u00fap hi\u1ec3u h\u00e0nh vi c\u1ee7a chuy\u00ean gia \u0111\u1ec3 thi\u1ebft k\u1ebf robot hi\u1ec7u qu\u1ea3 h\u01a1n v\u00e0 th\u00e2n thi\u1ec7n v\u1edbi con ng\u01b0\u1eddi h\u01a1n.<\/p>\n<\/li>\n<li>\n<p><strong>Xe t\u1ef1 l\u00e1i<\/strong>: IRL h\u1ed7 tr\u1ee3 suy \u0111o\u00e1n h\u00e0nh vi c\u1ee7a ng\u01b0\u1eddi l\u00e1i xe, cho ph\u00e9p c\u00e1c ph\u01b0\u01a1ng ti\u1ec7n t\u1ef1 \u0111\u1ed9ng \u0111i\u1ec1u h\u01b0\u1edbng an to\u00e0n v\u00e0 c\u00f3 th\u1ec3 d\u1ef1 \u0111o\u00e1n \u0111\u01b0\u1ee3c trong c\u00e1c t\u00ecnh hu\u1ed1ng giao th\u00f4ng h\u1ed7n h\u1ee3p.<\/p>\n<\/li>\n<li>\n<p><strong>H\u1ec7 th\u1ed1ng khuy\u1ebfn ngh\u1ecb<\/strong>: IRL c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng \u0111\u1ec3 m\u00f4 h\u00ecnh h\u00f3a s\u1edf th\u00edch c\u1ee7a ng\u01b0\u1eddi d\u00f9ng trong h\u1ec7 th\u1ed1ng \u0111\u1ec1 xu\u1ea5t, cung c\u1ea5p c\u00e1c \u0111\u1ec1 xu\u1ea5t ch\u00ednh x\u00e1c v\u00e0 \u0111\u01b0\u1ee3c c\u00e1 nh\u00e2n h\u00f3a h\u01a1n.<\/p>\n<\/li>\n<li>\n<p><strong>T\u01b0\u01a1ng t\u00e1c gi\u1eefa ng\u01b0\u1eddi v\u00e0 robot<\/strong>: IRL c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng \u0111\u1ec3 l\u00e0m cho robot hi\u1ec3u v\u00e0 th\u00edch \u1ee9ng v\u1edbi s\u1edf th\u00edch c\u1ee7a con ng\u01b0\u1eddi, gi\u00fap t\u01b0\u01a1ng t\u00e1c gi\u1eefa con ng\u01b0\u1eddi v\u00e0 robot tr\u1edf n\u00ean tr\u1ef1c quan h\u01a1n.<\/p>\n<\/li>\n<li>\n<p><strong>Th\u1eed th\u00e1ch<\/strong>: IRL c\u00f3 th\u1ec3 ph\u1ea3i \u0111\u1ed1i m\u1eb7t v\u1edbi nh\u1eefng th\u00e1ch th\u1ee9c trong vi\u1ec7c kh\u00f4i ph\u1ee5c ch\u1ee9c n\u0103ng ph\u1ea7n th\u01b0\u1edfng m\u1ed9t c\u00e1ch ch\u00ednh x\u00e1c, \u0111\u1eb7c bi\u1ec7t khi ph\u1ea7n tr\u00ecnh b\u00e0y c\u1ee7a chuy\u00ean gia b\u1ecb h\u1ea1n ch\u1ebf ho\u1eb7c \u1ed3n \u00e0o.<\/p>\n<\/li>\n<li>\n<p><strong>C\u00e1c gi\u1ea3i ph\u00e1p<\/strong>: Vi\u1ec7c k\u1ebft h\u1ee3p ki\u1ebfn th\u1ee9c mi\u1ec1n, s\u1eed d\u1ee5ng c\u00e1c khung x\u00e1c su\u1ea5t v\u00e0 k\u1ebft h\u1ee3p IRL v\u1edbi h\u1ecdc t\u1eadp t\u0103ng c\u01b0\u1eddng c\u00f3 th\u1ec3 gi\u1ea3i quy\u1ebft nh\u1eefng th\u00e1ch th\u1ee9c n\u00e0y.<\/p>\n<\/li>\n<\/ol>\n<h2>C\u00e1c \u0111\u1eb7c \u0111i\u1ec3m ch\u00ednh v\u00e0 c\u00e1c so s\u00e1nh kh\u00e1c v\u1edbi c\u00e1c thu\u1eadt ng\u1eef t\u01b0\u01a1ng t\u1ef1 d\u01b0\u1edbi d\u1ea1ng b\u1ea3ng v\u00e0 danh s\u00e1ch.<\/h2>\n<p>| H\u1ecdc t\u0103ng c\u01b0\u1eddng ng\u01b0\u1ee3c (IRL) so v\u1edbi H\u1ecdc t\u0103ng c\u01b0\u1eddng (RL) |<br \/>\n|\u2014\u2014\u2014\u2014\u2014\u2014 | \u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014\u2014-|<br \/>\n| IRL | RL |<br \/>\n| Suy ra ph\u1ea7n th\u01b0\u1edfng | Gi\u1ea3 s\u1eed ph\u1ea7n th\u01b0\u1edfng \u0111\u00e3 bi\u1ebft |<br \/>\n| H\u00e0nh vi gi\u1ed1ng con ng\u01b0\u1eddi | H\u1ecdc h\u1ecfi t\u1eeb nh\u1eefng ph\u1ea7n th\u01b0\u1edfng r\u00f5 r\u00e0ng |<br \/>\n| Kh\u1ea3 n\u0103ng gi\u1ea3i th\u00edch | \u00cdt minh b\u1ea1ch h\u01a1n |<br \/>\n| M\u1eabu hi\u1ec7u qu\u1ea3 | \u0110\u00f3i d\u1eef li\u1ec7u |<br \/>\n| Gi\u1ea3i quy\u1ebft ph\u1ea7n th\u01b0\u1edfng th\u01b0a th\u1edbt | \u0110\u1ea5u tranh v\u1edbi ph\u1ea7n th\u01b0\u1edfng th\u01b0a th\u1edbt |<\/p>\n<h2>C\u00e1c quan \u0111i\u1ec3m v\u00e0 c\u00f4ng ngh\u1ec7 c\u1ee7a t\u01b0\u01a1ng lai li\u00ean quan \u0111\u1ebfn h\u1ecdc t\u0103ng c\u01b0\u1eddng ngh\u1ecbch \u0111\u1ea3o.<\/h2>\n<p>T\u01b0\u01a1ng lai c\u1ee7a h\u1ecdc t\u0103ng c\u01b0\u1eddng ngh\u1ecbch \u0111\u1ea3o c\u00f3 nh\u1eefng b\u01b0\u1edbc ph\u00e1t tri\u1ec3n \u0111\u1ea7y h\u1ee9a h\u1eb9n:<\/p>\n<ol>\n<li>\n<p><strong>Thu\u1eadt to\u00e1n n\u00e2ng cao<\/strong>: Vi\u1ec7c ti\u1ebfp t\u1ee5c nghi\u00ean c\u1ee9u c\u00f3 th\u1ec3 s\u1ebd d\u1eabn \u0111\u1ebfn c\u00e1c thu\u1eadt to\u00e1n IRL hi\u1ec7u qu\u1ea3 v\u00e0 ch\u00ednh x\u00e1c h\u01a1n, gi\u00fap thu\u1eadt to\u00e1n n\u00e0y c\u00f3 th\u1ec3 \u00e1p d\u1ee5ng \u0111\u01b0\u1ee3c cho nhi\u1ec1u v\u1ea5n \u0111\u1ec1 h\u01a1n.<\/p>\n<\/li>\n<li>\n<p><strong>T\u00edch h\u1ee3p v\u1edbi Deep Learning<\/strong>: Vi\u1ec7c k\u1ebft h\u1ee3p IRL v\u1edbi c\u00e1c m\u00f4 h\u00ecnh h\u1ecdc s\u00e2u c\u00f3 th\u1ec3 mang l\u1ea1i h\u1ec7 th\u1ed1ng h\u1ecdc t\u1eadp m\u1ea1nh m\u1ebd h\u01a1n v\u00e0 s\u1eed d\u1ee5ng d\u1eef li\u1ec7u hi\u1ec7u qu\u1ea3 h\u01a1n.<\/p>\n<\/li>\n<li>\n<p><strong>\u1ee8ng d\u1ee5ng trong th\u1ebf gi\u1edbi th\u1ef1c<\/strong>: IRL d\u1ef1 ki\u1ebfn s\u1ebd c\u00f3 t\u00e1c \u0111\u1ed9ng \u0111\u00e1ng k\u1ec3 \u0111\u1ebfn c\u00e1c \u1ee9ng d\u1ee5ng trong th\u1ebf gi\u1edbi th\u1ef1c nh\u01b0 ch\u0103m s\u00f3c s\u1ee9c kh\u1ecfe, t\u00e0i ch\u00ednh v\u00e0 gi\u00e1o d\u1ee5c.<\/p>\n<\/li>\n<li>\n<p><strong>AI \u0111\u1ea1o \u0111\u1ee9c<\/strong>: Hi\u1ec3u \u0111\u01b0\u1ee3c s\u1edf th\u00edch c\u1ee7a con ng\u01b0\u1eddi th\u00f4ng qua IRL c\u00f3 th\u1ec3 g\u00f3p ph\u1ea7n ph\u00e1t tri\u1ec3n c\u00e1c h\u1ec7 th\u1ed1ng AI c\u00f3 \u0111\u1ea1o \u0111\u1ee9c ph\u00f9 h\u1ee3p v\u1edbi c\u00e1c gi\u00e1 tr\u1ecb c\u1ee7a con ng\u01b0\u1eddi.<\/p>\n<\/li>\n<\/ol>\n<h2>C\u00e1ch s\u1eed d\u1ee5ng ho\u1eb7c li\u00ean k\u1ebft m\u00e1y ch\u1ee7 proxy v\u1edbi vi\u1ec7c h\u1ecdc t\u0103ng c\u01b0\u1eddng ngh\u1ecbch \u0111\u1ea3o.<\/h2>\n<p>H\u1ecdc t\u0103ng c\u01b0\u1eddng ngh\u1ecbch \u0111\u1ea3o c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c t\u1eadn d\u1ee5ng trong b\u1ed1i c\u1ea3nh m\u00e1y ch\u1ee7 proxy \u0111\u1ec3 t\u1ed1i \u01b0u h\u00f3a h\u00e0nh vi v\u00e0 qu\u00e1 tr\u00ecnh ra quy\u1ebft \u0111\u1ecbnh c\u1ee7a ch\u00fang. M\u00e1y ch\u1ee7 proxy \u0111\u00f3ng vai tr\u00f2 trung gian gi\u1eefa m\u00e1y kh\u00e1ch v\u00e0 internet, \u0111\u1ecbnh tuy\u1ebfn c\u00e1c y\u00eau c\u1ea7u v\u00e0 ph\u1ea3n h\u1ed3i c\u0169ng nh\u01b0 cung c\u1ea5p t\u00ednh n\u0103ng \u1ea9n danh. B\u1eb1ng c\u00e1ch quan s\u00e1t h\u00e0nh vi c\u1ee7a chuy\u00ean gia, thu\u1eadt to\u00e1n IRL c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng \u0111\u1ec3 hi\u1ec3u s\u1edf th\u00edch v\u00e0 m\u1ee5c ti\u00eau c\u1ee7a kh\u00e1ch h\u00e0ng s\u1eed d\u1ee5ng m\u00e1y ch\u1ee7 proxy. Th\u00f4ng tin n\u00e0y sau \u0111\u00f3 c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng \u0111\u1ec3 t\u1ed1i \u01b0u h\u00f3a c\u00e1c ch\u00ednh s\u00e1ch v\u00e0 ra quy\u1ebft \u0111\u1ecbnh c\u1ee7a m\u00e1y ch\u1ee7 proxy, d\u1eabn \u0111\u1ebfn ho\u1ea1t \u0111\u1ed9ng proxy hi\u1ec7u qu\u1ea3 v\u00e0 hi\u1ec7u qu\u1ea3 h\u01a1n. Ngo\u00e0i ra, IRL c\u00f3 th\u1ec3 gi\u00fap x\u00e1c \u0111\u1ecbnh v\u00e0 x\u1eed l\u00fd c\u00e1c ho\u1ea1t \u0111\u1ed9ng \u0111\u1ed9c h\u1ea1i, \u0111\u1ea3m b\u1ea3o \u0111\u1ed9 tin c\u1eady v\u00e0 b\u1ea3o m\u1eadt t\u1ed1t h\u01a1n cho ng\u01b0\u1eddi d\u00f9ng proxy.<\/p>\n<h2>Li\u00ean k\u1ebft li\u00ean quan<\/h2>\n<p>\u0110\u1ec3 bi\u1ebft th\u00eam th\u00f4ng tin v\u1ec1 H\u1ecdc t\u0103ng c\u01b0\u1eddng ngh\u1ecbch \u0111\u1ea3o, b\u1ea1n c\u00f3 th\u1ec3 kh\u00e1m ph\u00e1 c\u00e1c t\u00e0i nguy\u00ean sau:<\/p>\n<ol>\n<li>\n<p>\u201cThu\u1eadt to\u00e1n h\u1ecdc t\u0103ng c\u01b0\u1eddng ngh\u1ecbch \u0111\u1ea3o\u201d c\u1ee7a Andrew Ng v\u00e0 Stuart Russell (2000).<br \/>\nLi\u00ean k\u1ebft: <a href=\"https:\/\/ai.stanford.edu\/~ang\/papers\/icml00-irl.pdf\" target=\"_new\" rel=\"noopener nofollow\">https:\/\/ai.stanford.edu\/~ang\/papers\/icml00-irl.pdf<\/a><\/p>\n<\/li>\n<li>\n<p>\u201cH\u1ecdc t\u0103ng c\u01b0\u1eddng ngh\u1ecbch \u0111\u1ea3o\u201d - M\u1ed9t b\u00e0i vi\u1ebft t\u1ed5ng quan c\u1ee7a Pieter Abbeel v\u00e0 John Schulman.<br \/>\nLi\u00ean k\u1ebft: <a href=\"https:\/\/ai.stanford.edu\/~ang\/papers\/icml00-irl.pdf\" target=\"_new\" rel=\"noopener nofollow\">https:\/\/ai.stanford.edu\/~ang\/papers\/icml00-irl.pdf<\/a><\/p>\n<\/li>\n<li>\n<p>B\u00e0i \u0111\u0103ng tr\u00ean blog OpenAI v\u1ec1 \u201cH\u1ecdc t\u1eadp c\u1ee7ng c\u1ed1 ngh\u1ecbch \u0111\u1ea3o t\u1eeb s\u1edf th\u00edch c\u1ee7a con ng\u01b0\u1eddi\u201d c\u1ee7a Jonathan Ho v\u00e0 Stefano Ermon.<br \/>\nLi\u00ean k\u1ebft: <a href=\"https:\/\/openai.com\/blog\/learning-from-human-preferences\/\" target=\"_new\" rel=\"noopener nofollow\">https:\/\/openai.com\/blog\/learning-from-human-preferences\/<\/a><\/p>\n<\/li>\n<li>\n<p>\u201cH\u1ecdc t\u0103ng c\u01b0\u1eddng ngh\u1ecbch \u0111\u1ea3o: Kh\u1ea3o s\u00e1t\u201d \u2013 Kh\u1ea3o s\u00e1t to\u00e0n di\u1ec7n v\u1ec1 c\u00e1c thu\u1eadt to\u00e1n v\u00e0 \u1ee9ng d\u1ee5ng IRL.<br \/>\nLi\u00ean k\u1ebft: <a href=\"https:\/\/arxiv.org\/abs\/1812.05852\" target=\"_new\" rel=\"noopener nofollow\">https:\/\/arxiv.org\/abs\/1812.05852<\/a><\/p>\n<\/li>\n<\/ol>","protected":false},"featured_media":468689,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-477698","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Inverse Reinforcement Learning: Unraveling the Hidden Rewards<\/mark>","faq_items":[{"question":"What is Inverse Reinforcement Learning (IRL)?","answer":"<p>Inverse Reinforcement Learning (IRL) is a branch of artificial intelligence that aims to understand an agent's underlying objectives by observing its behavior in a given environment. Unlike traditional reinforcement learning, where agents maximize predefined rewards, IRL infers the reward function from expert demonstrations, leading to more human-like decision-making.<\/p>"},{"question":"How did Inverse Reinforcement Learning originate?","answer":"<p>IRL was first introduced by Andrew Ng and Stuart Russell in their 2000 paper titled \"Algorithms for Inverse Reinforcement Learning.\" This seminal work laid the foundation for studying IRL and its applications in various domains.<\/p>"},{"question":"How does Inverse Reinforcement Learning work?","answer":"<p>The process of IRL involves observing an agent's behavior, recovering the reward function that best explains the behavior, and then optimizing the agent's policy based on the inferred rewards. IRL algorithms leverage expert demonstrations to uncover the underlying rewards, which can be used to improve decision-making processes.<\/p>"},{"question":"What are the key features of Inverse Reinforcement Learning?","answer":"<p>IRL offers several advantages, including a deeper understanding of human-like decision-making, transparency in reward functions, sample efficiency, and the ability to handle sparse rewards. It can also be used for transfer learning, where knowledge from one environment can be applied to a similar setting.<\/p>"},{"question":"What types of Inverse Reinforcement Learning exist?","answer":"<p>There are various types of IRL approaches, such as Maximum Entropy IRL, Bayesian IRL, Adversarial IRL, and Apprenticeship Learning. Each approach has its unique way of inferring the reward function from expert demonstrations.<\/p>"},{"question":"What are the applications of Inverse Reinforcement Learning?","answer":"<p>Inverse Reinforcement Learning finds applications in robotics, autonomous vehicles, recommendation systems, and human-robot interaction. It allows us to model and understand expert behavior, leading to better decision-making for AI systems.<\/p>"},{"question":"What are the challenges in using Inverse Reinforcement Learning?","answer":"<p>IRL may face challenges when recovering the reward function accurately, especially when expert demonstrations are limited or noisy. Addressing these challenges may require incorporating domain knowledge and using probabilistic frameworks.<\/p>"},{"question":"What does the future hold for Inverse Reinforcement Learning?","answer":"<p>The future of IRL is promising, with advancements in algorithms, integration with deep learning, and potential impacts on various real-world applications, including healthcare, finance, and education.<\/p>"},{"question":"How can Inverse Reinforcement Learning be associated with proxy servers?","answer":"<p>Inverse Reinforcement Learning can optimize the behavior and decision-making process of proxy servers by understanding user preferences and objectives. This understanding leads to better policies, improved security, and increased efficiency in the operation of proxy servers.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/wiki\/477698","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/wiki\/477698\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/media\/468689"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/media?parent=477698"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}