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• facile ri PDF version notare organization P5, P10 - P12
• joyrl comparationis supplementum P11-P13
• OpenAI documentum norma https://spinningup.openai.com/en/latest/index.html

Ultimae versionis PDF download
Oratio: https://github.com/datawhalechina/easy-rl/releases
Oratio domestica (commendatur lectoribus domesticis)：
Link: https://pan.baidu.com/s/1isqQnpVRWbb3yh83Vs0kbw Extraction code: us6a

facile-ri online version link (exemplum codice)
Relatio link 2: https://datawhalechina.github.io/joyrl-book/

alius;
[Errata record link]
——————
5. Basics of altum subsidii cognita
Aperto fonte contentum: https://linklearner.com/learn/summary/11
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Imago Source

Proximal consilium ipsum (PPO)

Consilium idem agens: Agens ad cognoscendum et agens interacting cum ambitu idem sunt.
Insidijs heterogeneis: agens ad discendum et agens commercium cum ambitu inter se differentes

Consilium Gradiente: requirit multum tempus sample data

eodem consilio $\stackrel{momenti\; sampling}{⟹}$ alia consilia

PPO: Vitare duas distributiones quae nimium differunt. eodem consilio algorithmus
1. Originale ipsum items$J(\theta ,{\theta }^{\prime })$
2. Coaint items:$\theta$ et${\theta }^{\prime }$ KL distinctio actionis output$\theta$ et${\theta }^{\prime }$ quo similior melior)

PPO decessorem habet: fiducia regionis consilium optimiization (TRPO)
TRPO tractari difficile est, quia de KL disiunctionem coercitionem additivam coactionem tractat et in functione obiectiva non ponitur, ideo computare difficile est. Itaque vulgo pro TRPO PPO utimur. Effectus PPO et TRPO similes sunt, sed PPO multo facilius est ad efficiendum quam TRPO.

KL distinctio: distantia agendi.Probabilitas distributionem faciendo actionem procul.

Duae sunt praecipuae variantes algorithmi PPO: consilium proximale optimization poena (PPO-poena) et consilium proximalis optimiizationis detonsio (PPO-clip).

——————————
P10 Sparse praemium quaestio
1. Dedo praemia. Scientia requirit domain
Quid de ultimo praemio ad singulas actiones pertinentes assignare?

2. Curiositas
Curiositas intrinseca moduli (ICM)
introire;$a_t,s_t$
Output:${\hat{s}}_{t+1}$
Predicta valorem de network${\hat{s}}_{t+1}$ cum vero valorem$s_{t+1}$ Magis dissimiles sunt,${r*******************************************************************************}_t^{ego}$ Maior

${r*******************************************************************************}_t^{ego}$ Difficilior res publica futura praedicit, eo maior merces est. Ac exploratio robora casus.

• Nimis simplex index est, et inutilia scias modo.

pluma extractor

Retis 2:
Input: vector$\mathbf{\varphi }(s_t)$ et$\mathbf{\varphi }(s_{t+1})$

Praedic actio $\hat{a}$ Quo propius ad rem agendam melior.

3. cursus studio

Securus -> Difficilis

Curriculum discendi inversa;
Proficiscens ab ultimo statu optimo maximo, iReperio publica proxima ad statum aureum Sicut a ridiculo "ideale" dicitur quod vis agentis attingere. Nempe in hoc processu aliquos status extremas ex intentione removebimus, id est, res nimis faciles vel nimis difficiles.

4. supplementum doctrinale hierarchicum (HRL)
Agens consilium dividitur in consilia alta et humili gradu consilia.

————————
P11 doctrina Imitatio
Non certa circa praemium scaena

Imitatio discendi (IL)
doctrina ex demonstratione
Tirocinium doctrina
doctrina a vigilantes

Luculenta sunt praemia: tabulae ludi, ludos video
Praemia clara dare non possunt: ​​chatbot

Collige demonstrationes peritos: monumenta incessus humana, colloquia humana

Inverse quale praemium munus accipit has actiones peritus?
Auxilia inversa doctrina estPrimum munus invenire praemiumPost munus munus nactus, supplementum studiorum ad meliorem actorem invenire.

Tertius homo imitatio doctrinarum technologiarum

————————
Profundum deterministic consilium CLIVUS P12 (DDPG)

Usus belli replay experientia

Ablatio Experimenti [Modus Controlled Variabilis] Analysisomni necessitateeventum pugnae infringere.

---

joyrl：

DDPG_continuous

in inopiacertitudoet bellicontinua actioSub praemissa spatio algorithmus hoc genus algorithmus basi relative stabilis erit.

DQN ad continuam actionem spatia

Algorithmus in altum consilium deterministic gradiente (DDPG)

Experientia remonstrandi mechanismum reducere potest ad comparationem inter exempla, efficaciam exemplorum usum emendare et stabilitatem institutionis augere.

Defectus;
1. non potest esse in discreta actio spatium
2、Maxime dependens hyperparametris
3. Valde sensitivae condiciones initiales. Afficit concursum et observantiam algorithmi
4. Facile est in loci optimum cadere.

• Ob adoptionem consilii deterministici, algorithmus in optimam localem cadere potest et difficilem in global meliorem belli rationem invenire. Ad explorabilitatem augendam, quaedam mensurae accipienda sunt, ut rationes strepitus addendo vel aliis methodis explorationis adhibendis.

Commodum mollium renovationum est quod levius est et tardius, quae potest vitare impulsus per nimias celeritatis momenta et periculum disciplinae discrepantiae minuere.

Duplex moratus consilium deterministicum algorithmus gradiens (gemini moratus DDPG, TD3).

Duplex mora consilium deterministicum gradiente algorithmus

Tres emendationes: Duplex Q retis, renovatio morata, strepitus regularization
Duplex Q Network : Duo Q retiacula, elige unum cum valore minore Q. Agere de superestimatione problematis Q valoris et stabilitatis et concursus algorithmi melioris.

Dilatio renovatio: Actor renovatio frequency ut minor sit quam renovatio critica frequency

• Cogitare bis

Sonus similior estOrdinationisita utvalorem munus updateplussmooth

OpenAI Gym Library_Pendulum_TD3

Documentum OpenAI de nexus interface TD3

TD3 charta PDF link

PPO_Continuous/Discrete Action Space [OpenAI 201708]

Frequentissime PPO algorithmus in supplementi studiorum usus est
Discreta + continua
Jejunium et stabile, facile parametris accommodare
collocantur algorithmus

anceps PPO

In praxi, cohibere angustias plerumque adhibentur, quia simplicior est, inferiores sumptus computationales et meliores proventus habet.

In off-consilium algorithmus canutantur historica experientiaplerumque usu remonstrandi usus est ad reponendas ac reuse peritia praecedens;Data utendo efficientiam est princeps。

PPO est on-consilium algorithmus

• Licet momenti sampling parte utitur exemplaria a vetere actore sampling, nosExempla haec non directe ad consilium renovandum adhibita sunt. sed momenti sampling usus est ut errores ex diversis notitiis diversimode corrigantur, etiamsi differentia duarum distributionum quam maxime reducatur.Aliis verbis, intelligi potest, quod, licet exempla, post momentum sampling, ab antiquo consilio capiantur, possuntProxime adeptus est a updated consiliumid est, actorem volumus optimize et actorem, quem specimen sumus.

——————————————————

-- OpenAI Documentation_PPO

OpenAI Documenta
Paper arXiv interface link: Proximal Policy Optimization Algorithms

PPO: algorithmus on-consilium, ad discreta vel continua actionis spatia apta.Maxime loci fieri

Causa PPO idem est ac TRPO: quomodo leverage existens notitiaUt maximus gradus fieri potest emendationem in bellisine mutando nimis et fortuito faciens fragorem?
TRPO hanc quaestionem solvere conatur cum accessione secundi ordinis sapientissimi, dum PPO est ordo primi ordinis qui utitur quibusdam aliis technis ad novum consilium proxime antiquum custodiendum.
PPO methodus multo simplicior est ad efficiendum et empirice faciendum saltem ac TRPO.

Duae sunt differentiae principales PPO: PPO-Poena et PPO-Clip.

• PPO-Poena proxime KL angustias updates sicut TRPO solvit, sed KL-diverentiam in functione obiectiva punit pro dura coactione faciens, et automatice poenam coëfficientem in exercitatione aptat ut convenienter scandet.
• PPO-Clip nullas habet KL-diversitas nec angustias in functione obiectiva. Sed certas tailoring obiectivae functionis nititur ad removendum incitamentum novi consilii ad movendum a veteri consilio.
  PPO-Clip (praecipua variantia ab OpenAl adhibita).

PPO-Clip algorithmus pseudo code

Algorithmus: PPO-Clip
I: Input: parametri belli initial${\theta }_0$, Munus valorem parametri ${\varphi }_0$
2：$for**********k=0,1,2,\dots \mathbf{do}$：
3：        ~~~~~~       Per consilium currit in environment ${\pi }_k=\pi ({\theta }_k)$ Collecta trajectoriam posuit D k = { τ i } {cal D}_k={tau_i}Dk​={τego​}
4：        ~~~~~~       Praemia computare (praemia ut- ite) ${\hat{R}}_t$ ▢ ${\hat{R}}_t$ calculi praecepta
5：        ~~~~~~       Computo commodum aestimandum, ex valore currenti functionis $V_{{\varphi }_k}$ of*${\hat{A}}_t$ (Utere aliqua aestimatione dominatum modum)       ~~~~~       Quae sunt hodiernae commoda aestimationis modi?
6：        ~~~~~~       Renova consilium ab maximising PPO-Clip munus obiectivum:
            ~~~~~~~~~~~            
θ k + 1 = arg ⁡ max ⁡ θ 1 D k ∣ T ∑ τ ∈ D k ∑ t = 0 T min ⁡ ( π θ ( at st ) π θ k ( at st ) A π θ k ( st. ) , g ( , A π θ k ( st , at ) ) ~~~~~~~~~~~ theta_{k+1}=argmaxlimits_thetafrac{1}{|{cal D}_k|T } sumlimits_{tauin{cal D}_k}sumlimits_{t=0}^TminBig(frac{pi_{theta} (a_t|s_t)}{pi_{theta_k}(a_t|s_t)}A^{pi_{theta_k}} (s_t,a_t),g(epsilon,A^{pi_{theta_k}}(s_t,a_t))Big)           θk+1​=arg*********θmax​∣Dk​∣T1​τ∈Dk​∑​t=0∑T​min(πθk​​(at​∣st​)πθ​(at​∣st​)​Aπθk​​(st​,at​),g*********(ϵ,Aπθk​​(st​,at​)))       ~~~~~       Quomodo definire renovationem belli formulam?
            ~~~~~~~~~~~            
            ~~~~~~~~~~~             ${\pi }_{{\theta }_k}$ : Consilium parametri vectoris ante renovationem. Sampling momentum. Sampling ab antiquis insidijs.
            ~~~~~~~~~~~            
            ~~~~~~~~~~~            General Stochastic Gradiente Ascensu + Adam
7：        ~~~~~~       quadrata medium errorisprocedere valorem aptavit munus:
            ~~~~~~~~~~~            
k + 1 = arg min 1 D k ∣ T ∑ τ D k ∑ t = 0 T ( V ( st ) R ^ t ) 2 ~~~~~~~~~~~~~ phi_ {k+1}=arg minlimits_phifrac{1}{|{cal D}_k|T} sumlimits_{tauin{cal D}_k}sumlimits_{t=0}^TBig(V_phi(s_t)-hat R_tBig)^2           ϕk+1​=arg*********ϕmin​∣Dk​∣T1​τ∈Dk​∑​t=0∑T​(Vϕ​(st​)−R^t​)2
            ~~~~~~~~~~~            
            ~~~~~~~~~~~            General descensus
8：$\mathbf{finis}for**********$
             ~~~~~~~~~~~~             

$dots$$\dots$

$g*********(ϵ,A)=\{\begin{array}{rlr}& (1+ϵ)A& A\ge 0\\ & (1-ϵ)A& A<0\end{array}$

in chartaUtilitas aestimatio:

${\hat{A}}_t=-V(s_t)+\underset{{\hat{R}}_t???}{\underbrace{{r*******************************************************************************}_t+\gamma {r*******************************************************************************}_{t+1}+\cdots +{\gamma }^{T-t+1}{r*******************************************************************************}_{T-1}+{\gamma }^{T-t}V(s_T)}}$

facere ${\Delta }_t={r*******************************************************************************}_t+\gamma V(s_{t+1})-V(s_t)$
sed${r*******************************************************************************}_t={\Delta }_t-\gamma V(s_{t+1})+V(s_t)$

Substitutus ${\hat{A}}_t$ expressio

$\begin{array}{rl}{\hat{A}}_t& =-V(s_t)+{r*******************************************************************************}_t+\gamma {r*******************************************************************************}_{t+1}+{\gamma }^2{r*******************************************************************************}_{t+2}+\cdots +{\gamma }^{T-t}{r*******************************************************************************}_{T-2}+{\gamma }^{T-t+1}{r*******************************************************************************}_{T-1}+{\gamma }^{T-t}V(s_T)\\ & =-V(s_t)+{r*******************************************************************************}_t+\gamma {r*******************************************************************************}_{t+1}+\cdots +{\gamma }^{T-t+1}{r*******************************************************************************}_{T-1}+{\gamma }^{T-t}V(s_T)\\ & =-V(s_t)+\\ & {\Delta }_t-\gamma V(s_{t+1})+V(s_t)+\\ & \gamma ({\Delta }_{t+1}-\gamma V(s_{t+2})+V(s_{t+1}))+\\ & {\gamma }^2({\Delta }_{t+2}-\gamma V(s_{t+3})+V(s_{t+1}))+\\ & \cdots +\\ & {\gamma }^{T-t}({\Delta }_{T-t}-\gamma V(s_{T-t+1})+V(s_{T-t}))+\\ & {\gamma }^{T-t+1}({\Delta }_{T-1}-\gamma V(s_T)+V(s_{T-1}))+\\ & {\gamma }^{T-t}V(s_T)\\ & ={\Delta }_t+\gamma {\Delta }_{t+1}+{\gamma }^2{\Delta }_{t+2}+\cdots +{\gamma }^{T-t}{\Delta }_{T-t}+{\gamma }^{T-t+1}{\Delta }_{T-1}\end{array}$

Tonsio fungitur ordinatoris subtrahendo incitamentum ad mutationes acris consilii.hyperparameters $ϵ$ Respondet distantiae inter novum consilium et consilium vetus。

Potest tamen fieri ut hoc genus detonsionis tandem eveniat in novo consilio, quod longe est a consilio veteri.Subsisto diluculo . Si mediocris KL-diversitas novi consilii a vetere consilio limen excedit, gradatim progredi prohibemus.

PPO munus obiectivum simplex derivatio paginae
Munus obiectivum PPO-Clip est:
  ~  
$L_{{\theta }_k}^{\mathrm{CLIP}}(\theta )=\underset{s,a\sim {\theta }_k}{E}\left[\min \right(\frac{{\pi }_{\theta }(a|s)}{{\pi }_{{\theta }_k}(a|s)}{A}^{{\theta }_k}(s,a),\mathrm{tonde}(\frac{{\pi }_{\theta }(a|s)}{{\pi }_{{\theta }_k}(a|s)},1-ϵ,1+ϵ)A^{{\theta }_k}(s,a)\left)\right]$
  ~  
$underset{s, asimtheta_k}{rm E}$$\underset{s,a\sim {\theta }_k}{E}$
  ~  
Nec.$k$ Belli parametri iterations${\theta }_k$， $ϵ$ hyperparameter parvum est.
extruxerat$ϵ\in (0,1)$, definition
$F(r*******************************************************************************,A,ϵ)\doteq \min (r*******************************************************************************A,\mathrm{tonde}(r*******************************************************************************,1-ϵ,1+ϵ)A)$
quando $A\ge 0$
$\begin{array}{rl}F(r*******************************************************************************,A,ϵ)& =\min (r*******************************************************************************A,\mathrm{tonde}(r*******************************************************************************,1-ϵ,1+ϵ)A)\\ & =A\min (r*******************************************************************************,\mathrm{tonde}(r*******************************************************************************,1-ϵ,1+ϵ))\\ & =A\min (r*******************************************************************************,\left\{\begin{array}{rlr}& 1+ϵ& r*******************************************************************************\ge 1+ϵ\\ & r*******************************************************************************& r*******************************************************************************\in (1-ϵ,1+ϵ)\\ & 1-ϵ& r*******************************************************************************\le 1-ϵ\end{array}\right\})\\ & =A\left\{\begin{array}{rlr}& \min (r*******************************************************************************,1+ϵ)& r*******************************************************************************\ge 1+ϵ\\ & \min (r*******************************************************************************,r*******************************************************************************)& r*******************************************************************************\in (1-ϵ,1+ϵ)\\ & \min (r*******************************************************************************,1-ϵ)& r*******************************************************************************\le 1-ϵ\end{array}\right\}\\ & =A\left\{\begin{array}{rlr}& 1+ϵ& r*******************************************************************************\ge 1+ϵ\\ & r*******************************************************************************& r*******************************************************************************\in (1-ϵ,1+ϵ)\\ & r*******************************************************************************& r*******************************************************************************\le 1-ϵ\end{array}\right\}Secundum\; range\; in\; dextro\\ & =A\min (r*******************************************************************************,1+ϵ)\\ & =\min (r*******************************************************************************A,(1+ϵ)A)\end{array}$
  ~  
quando $A<0$
$\begin{array}{rl}F(r*******************************************************************************,A,ϵ)& =\min (r*******************************************************************************A,\mathrm{tonde}(r*******************************************************************************,1-ϵ,1+ϵ)A)\\ & =Amax*(r*******************************************************************************,\mathrm{tonde}(r*******************************************************************************,1-ϵ,1+ϵ))\\ & =A\max (r*******************************************************************************,\left\{\begin{array}{rlr}& 1+ϵ& r*******************************************************************************\ge 1+ϵ\\ & r*******************************************************************************& r*******************************************************************************\in (1-ϵ,1+ϵ)\\ & 1-ϵ& r*******************************************************************************\le 1-ϵ\end{array}\right\})\\ & =A\left\{\begin{array}{rlr}& \max (r*******************************************************************************,1+ϵ)& r*******************************************************************************\ge 1+ϵ\\ & \max (r*******************************************************************************,r*******************************************************************************)& r*******************************************************************************\in (1-ϵ,1+ϵ)\\ & \max (r*******************************************************************************,1-ϵ)& r*******************************************************************************\le 1-ϵ\end{array}\right\}\\ & =A\left\{\begin{array}{rlr}& r*******************************************************************************& r*******************************************************************************\ge 1+ϵ\\ & r*******************************************************************************& r*******************************************************************************\in (1-ϵ,1+ϵ)\\ & 1-ϵ& r*******************************************************************************\le 1-ϵ\end{array}\right\}Secundum\; range\; in\; dextro\\ & =A\max (r*******************************************************************************,1-ϵ)\\ & =\mathrm{megon}(r*******************************************************************************A,(1-ϵ)A)\end{array}$
  ~  
Perorare: quidd$g*********(ϵ,A)$
$g*********(ϵ,A)=\{\begin{array}{rlr}& (1+ϵ)A& A\ge 0\\ & (1-ϵ)A& A<0\end{array}$

Cur haec definitio impedit novum consilium ne a consilio antiquo longius evadat?
Efficax momenti sampling modi novas strategies require ${\pi }_{\theta }(a|s)$ et vetera consilia${\pi }_{{\theta }_k}(a|s)$ Differentia duarum distributionum nimis magna esse non potest

1. Cum utilitas affirmativa

$L(s,a,{\theta }_k,\theta )=\min (\frac{{\pi }_{\theta }(a|s)}{{\pi }_{{\theta }_k}(a|s)},1+ϵ)A^{{\pi }_{{\theta }_k}}(s,a)$
Munus commodum: Invenire certas actiones par cum pluribus praemiis -&gt; auge pondus status-actionis par.

Cum status-actio par $(s,a)$ positivum, si actio$a$ supplicium verisimilius est, i.e${\pi }_{\theta }(a|s)$ Augeatur et augebit finis.
min in hac item munus obiectivum limitat ad certum valorem tantum augendum
semel${\pi }_{\theta }(a|s)>(1+ϵ){\pi }_{{\theta }_k}(a|s)$, min triggers, valorem item to huius limitis $(1+ϵ){\pi }_{{\theta }_k}(a|s)$。
non prosit nova consilia longe a vetere consilio eundo.
Novum consilium ab antiquo consilio regredi non prodest.

2. Cum utilitas negativa

$L(s,a,{\theta }_k,\theta )=\max (\frac{{\pi }_{\theta }(a|s)}{{\pi }_{{\theta }_k}(a|s)},1-ϵ)A^{{\pi }_{{\theta }_k}}(s,a)$

Cum status-actio par $(s,a)$ Utilitas negativa est, si actio$a$ id est, si${\pi }_{\theta }(a|s)$ diminui, munus obiectivum augere. Max autem in hoc termino limites quantum munus obiectivum augeri potest.
semel${\pi }_{\theta }(a|s)<(1-ϵ){\pi }_{{\theta }_k}(a|s)$, triggers max, valore huius item in limitando $(1-ϵ){\pi }_{{\theta }_k}(a|s)$。

Item: Novum consilium non prodest longe abesse a vetere consilio.
Novum consilium ab antiquo consilio regredi non prodest.

TD3_nisi continuati: Didymus Moratus Profundum Deterministic Policy Gradient [ICML 2018 (Canada) McGill University]

Imago Source

OpenAI Documentation_TD3
Paper link

Dum DDPG interdum excellentem observantiam consequi potest, saepe instabilis est cum ad hyperparametris et alias species incedit.
Communis DDPG defectus modus est quod doctus Q munus incipit signanter aestimare valorem Q, quod tunc consilium frangere facit quod errorem in Q functione gerit.
Didymus Moratus DDPG (TD3) est algorithmus qui hanc quaestionem solvit tribus technicis clavis inductis:
1、Truncata duplex Q Doctrina。

• TD3 discit duas Q functiones loco unius (unde "gemini") et utitur minoribus duobus Q valoribus ut scopum in Bellman errore functionis amissionis formare.

2、Consilium update mora。

• TD3 consilium (et scopum retis) minus saepe quam munus Q. Charta suadet adaequationis consilium omni tempore munus Q bis renovatum est.

3. Scopum consiliorum delenimenta.

• TD3 sonitum actionis scopo addit, difficiliorem reddens consilium ut errores in Q functione ficendo per mutationes actionis Q leniendo.

TD3 algorithmus off-consilium est;continuusAmbitu actionis spatium.

TD3 algorithmus pseudo code

Algorithmus: TD3
Utere temere parametri${\theta }_1,{\theta }_2,\varphi$ Initialize critica network$Q_{{\theta }_1},Q_{{\theta }_2}$Et actorem retis ${\pi }_{\varphi }$
Initialize scopum network${\theta }_1^{\prime }\leftarrow {\theta }_1,{\theta }_2^{\prime }\leftarrow {\theta }_2,{\varphi }^{\prime }\leftarrow \varphi$
Initialize playback quiddam set$\mathcal{B}$
$for**********t=1\mathbf{to}T$ ：
       ~~~~~~       Select actio cum strepitu exploratio $a\sim {\pi }_{\varphi }(s)+ϵ,ϵ\sim \mathcal{N}(0,\sigma )$, observationis praemium $r*******************************************************************************$ ac novus status$s^{\prime }$
       ~~~~~~       Transitus tuple $(s,a,r*******************************************************************************,s^{\prime })$ deposit to$\mathcal{B}$ medium
       ~~~~~~       e* $\mathcal{B}$ Sampling parva batches$N$ transitus$(s,a,r*******************************************************************************,s^{\prime })$
$\tilde{a}\leftarrow {\pi }_{{\varphi }^{\prime }}(s^{\prime })+ϵ,ϵ\sim \mathrm{tonde}(\mathcal{N}(0,\tilde{\sigma }),-c**,c**)$
$y**\leftarrow r*******************************************************************************+\gamma \underset{ego=1,2}{\min }{Q}_{{\theta }_{ego}^{\prime }}(s^{\prime },\tilde{a})$
       ~~~~~~       Updatecritics θ i ← arg ⁡ min θ i N 1 ∑ ( y Q θ i ( s , a ) ) ^2θego​←arg*********θego​min​N−1∑(y**−Qθego​​(s,a))2
       ~~~~~~        $\mathbf{si}t\%d*$：
            ~~~~~~~~~~~            Renova per deterministic consilium gradiente $\varphi$
${\nabla }_{\varphi }J(\varphi )=N^{-1}\sum {\nabla }_a{Q}_{{\theta }_1}(s,a){|}_{a={\pi }_{\varphi }(s)}{\nabla }_{\varphi }{\pi }_{\varphi }(s)$
            ~~~~~~~~~~~            Renova scopum retis:
${\theta }_{ego}^{\prime }\leftarrow \tau {\theta }_{ego}+(1-\tau ){\theta }_{ego}^{\prime }$$\tau$: Target update rate
${\varphi }^{\prime }\leftarrow \tau \varphi +(1-\tau ){\varphi }^{\prime }$
$\mathbf{finis}\mathbf{si}$
$\mathbf{finis}for**********$

Mollis Actor-Criticus: SAC_Continuous/Discrete Action Space [Google Brain latest version 201906]

Imago Source

Maximize entropy of the policy, thus making the policy robustior.

determinatici belli Significat eodem statu data, semper eandem actionem eligere
fortuiti belli Significat quod multae sunt actiones possibiles quae in aliqua civitate possunt eligi.

In ipsa applicatione, si condiciones permittunt, volumusTry utfortuiti belliut A2C, PPO, etc., quia flexibilior, robustior & stabilior est.

Maximum entropy supplementi discendi credit quod, quamvis nunc fortuiti consilia matura habeamus, algorithmos scilicet ac AC, tamen optimales fortuiti non sumus consecuti.Ideo inducit anotitia entropyconceptum, inPraemium cumulativum maximize dum maxima entropy of the policybatque consilium robustiorem et ad meliorem fortuiti belli rationem assequendam.

——————————————————

-- OpenAI Documentation_SAC

OpenAI Documentation_SAC Interface Link
  ~  
Mollis Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning cum Stochastic Actor, Haarnoja et al, 201808 ICML 2018
Mollis Actor-Criticus Algorithmus et Applications, Haarnoja et al, 201901
Ut ambulare per altum Reinforcement Learning, Haarnoja et al, 201906 RSS2019

Mollis Actor Criticus (SAC) optimizes strategies temere in modo off-consilii.

DDPG + stochastic belli optimization

Haud immediatus successor TD3 (per idem tempus dimissus est).

Dolum duplicem-Q tonsum incorporat, et ob fortuiti SAC instrumenti inhaerens, etiam tandem prodest ex.scopum consilium delenimenta。

A core pluma est SAC entropy regularization entropy regularization。
Consilium exercetur ad mercaturam maximizandam inter exspectationem mercedis et entropy;Entropy est mensura fortuiti consilii。
Hoc finitimum est commercio inter explorationem et abusionem: augmentum entropy ducit adMagis explorandumHoc est OKAccelerare subsequent doctrina .Bene estPreoccupo consilium de malis intempestive convergentibus ad loci optimam。

Adhiberi potest in utroque spatio continua actio, et discreta actio spatii.

exist Entropy regularized Reinforcement Learningagens obtinetEntropy of the policy at this time stepPraemia proportionalia.
Hoc tempore quaestio RL sic describitur:

π = arg ⁡ max π E τ ∼ π [ t = 0 ∞ γ t ( R ( st , at , st + 1 ) + α H ( π ( st ) ) ) ] pi^*= argmaxlimits_pi underset {tausimpi}{rm E}Big[sumlimits_{t=0}^inftygamma^tBig(R(s_t,a_t,s_{t+1})textcolor{hyacinthinum}{+alpha H(pi(·|s_t))} Big)Big]π∗=arg*********πmax​τ∼πE​[t=0∑∞​γt(R(st​,at​,st+1​)+αH(π(⋅∣st​)))]

in $\alpha >0$ est commercium-off coefficiens.
Munus rei publicae valorem entropy comprehendo merces singulis diebus gradus$V^{\pi }$ ad:

$V^{\pi }(s)=\underset{\tau \sim \pi }{E}\left[\sum _{t=0}^{\infty }{\gamma }^t\right(R(s_t,a_t,s_{t+1})+\alpha H(\pi (\cdot |s_t)))|s_0=s]$

Actio valoris functionis quae entropy includit praemium pro omni tempore gradus praeter primum gradum $Q^{\pi }$:

$Q^{\pi }(s,a)=\underset{\tau \sim \pi }{E}\left[\sum _{t=0}^{\infty }{\gamma }^t\right(R(s_t,a_t,s_{t+1})+\alpha \sum _{t=1}^{\infty }H(\pi (\cdot |s_t)))|s_0=s,a_0=a]$

• quidam papers $Q^{\pi }$ Praemium primum gradum continet entropy

$V^{\pi }$ et$Q^{\pi }$ Necessitudo est:

$V^{\pi }(s)=\underset{a\sim \pi }{E}[Q^{\pi }(s,a)]+\alpha H(\pi (\cdot |s))$

de " $Q^{\pi }$ Formula Bellman est;

$\begin{array}{rl}{Q}^{\pi }(s,a)& =\underset{\genfrac{}{}{0ex}{}{s^{\prime }\sim P}{a^{\prime }\sim \pi }}{E}[R(s,a,s^{\prime })+\gamma (Q^{\pi }(s^{\prime },a^{\prime })+\alpha H(\pi (\cdot |s^{\prime })))]\\ & =\underset{s^{\prime }\sim P}{E}[R(s,a,s^{\prime })+\gamma V^{\pi }(s^{\prime })]\end{array}$

SAC discit consilium eodem tempore ${\pi }_{\theta }$ et duo$Q$ officium$Q_{{\varphi }_1},Q_{{\varphi }_2}$。
Adsunt duae variantes vexillum SAC: una certa utiturEntropy regularization coefficientis $\alpha$alius, mutando in disciplina $\alpha$ exeat entropy angustiis.
Documenta OpenAI versione utitur cum coefficiente certo entropy regularizationis, sed in usu saepe praefertur.entropy angustiavariant.

Ut infra, in $\alpha$ In versione fixa, praeter ultimam picturam, quae perspicuas utilitates habet, ceterae leves tantum utilitates habent, plerumque eadem$\alpha$ Eadem litera discendi manet;$\alpha$ Duae imagines mediae sunt ubi literae literae utilitates manifestiores sunt.

Imago Source

SAC VSTD3:
  ~  
Eodem loco:
1. Ambae Q functiones discuntur extenuando MSBE (Error Mean Squared Bellman) per regressionem ad unum objectum commune.
2. Utere scopo Q-retis ad scopum commune computare, et polyak fere in parametris Q-retis per processum disciplinae ad obtinendum scopum Q-retis.
3. Communis clypei Q ars duplici mutila utitur.
  ~  
differentia;
1. SAC continet entropy regularization terminus
2. Sequens actio publica usus in SAC metam venit exCurrent bellipotius quam scopo consilio.
3. Nulla patet scopo militaris ad delenimenta. TD3 docet determinatum consilium per actiones ad proximum statumAdde temere sonitus consequatur blanditiis ut. SAC impedimenta temere consilium, et strepitus fortuiti satis est effectus similes efficere.

SAC algorithmus pseudo code

Algorithmus: Mollis Actor-Criticus SAC
introire;${\theta }_1,{\theta }_2,\varphi$ Initialization parametri
Parameter initialization:
       ~~~~~~       Initialize scopum network weights:${\bar{\theta }}_1\leftarrow {\theta }_1,{\bar{\theta }}_2\leftarrow {\theta }_2$
       ~~~~~~       Piscina playback initialized vacua est;$\mathcal{D}\leftarrow \varnothing$
$for**********$ per iterationem$\mathbf{do}$ ：
       ~~~~~~       $for**********$ Quisque amet gradum$\mathbf{do}$ ：
            ~~~~~~~~~~~            Ac- tiones specimen ex consilio: $a_t\sim {\pi }_{\varphi }(a_t|s_t)$ Hic ${\pi }_{\varphi }(a_t|s_t)$ Quomodo definis?
            ~~~~~~~~~~~            Sample transitus e ambitu:$s_{t+1}\sim p(s_{t+1}|s_t,a_t)$
            ~~~~~~~~~~~            Serva transitus ad piscinam playback: $\mathcal{D}\leftarrow \mathcal{D}\cup \{(s_t,a_t,r*******************************************************************************(s_t,a_t),s_{t+1})\}$
       ~~~~~~       $\mathbf{finis}for**********$
       ~~~~~~       $for**********$ Quisque gradus gradiente$\mathbf{do}$ ：
            ~~~~~~~~~~~            renovare $Q$ Munus parametri: nam i { 1, 2 } iin{1,2}ego∈{1,2}，${\theta }_{ego}\leftarrow {\theta }_{ego}-{\lambda }_Q{\hat{\nabla }}_{{\theta }_{ego}}{J}_Q({\theta }_{ego})$ Hic $J_Q({\theta }_{ego})$ Quomodo definis?
            ~~~~~~~~~~~            Pondera update belli: $\varphi \leftarrow \varphi -{\lambda }_{\pi }{\hat{\nabla }}_{\varphi }{J}_{\pi }(\varphi )$ Hic $J_{\pi }(\varphi )$ Quomodo definis?
            ~~~~~~~~~~~            Adjust tortor: $\alpha \leftarrow \alpha -\lambda {\hat{\nabla }}_{\alpha }J(\alpha )$ Hic $J(\alpha )$ Quomodo definis?Quomodo intellegendum est hie temperatus?
            ~~~~~~~~~~~             Renova scopum network weights: nam i { 1, 2 } iin{1,2}ego∈{1,2}，${\bar{\theta }}_{ego}\leftarrow \tau {\theta }_{ego}-(1-\tau ){\bar{\theta }}_{ego}$ Quomodo intelligere hoc $\tau$ ? --&gt; Target delenimenta coefficientis
       ~~~~~~       $\mathbf{finis}for**********$
$\mathbf{finis}for**********$
Output:${\theta }_1,{\theta }_1,\varphi$Optimized parametri

$\hat{\nabla }$: Stochastic CLIVUS

$emptyset$$\varnothing$

Ut ambulare per altum Reinforcement Learning Versio in:
  ~  

$\alpha$ modulus temperatus est, qui relativum momenti entropy terminus et merces determinat, fortuiti optimalis consilii moderatur.
$\alpha$ Magna: Explore
$\alpha$ Parvus: facinus

J ( α ) = E at ∼ π t [ α log ⁡ π t ( at st ) α H ] J(alpha)=intellectu{a_tsimpi_t}{ mathbb E}[-alphalog pi_t(a_t|s_t)- alphabar{cal H}]J(α)=at​∼πt​E​[−αlog*********πt​(at​∣st​)−αHˉ]