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# System prepended metadata

title: 'Large Sequence Models for Decision Making: A Survey'

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# Large Sequence Models for Decision Making: A Survey

### 1 Introduction

### 2 Formulation
#### 2.1 Prediction Tasks (CV/NLP)
#### 2.2 The Transformer 
#### 2.3 Decision-Making Tasks

### 3 Conventional RL for Decision-Making
#### 3.1 Sampling Cost (drawback)
#### 3.2 Credit Assignment (drawback)
#### 3.3 Partial Observability (drawback)

### 4 Sequence Models for Decision-Making
#### 4.1 Sampling Cost (adv)
#### 4.2 Credit Assignment (adv)
#### 4.3 Partial Observability (adv)

### 5 Why Transformer (name to be determined)

### 6 How Sequence Transformers for DM (Current Methods)
#### 6.1 Transformers For Policy
##### 6.1.1 For Representation
##### 6.1.2 For Pre-training
#### 6.2 Transformers For World Model
#### 6.3 Transformers For Reward Function
#### 6.4 ....(knowledge resourse/improvement method)

### 7 Training System(Option A)
#### 5.1 General challenges(reqirements and trends in model)
#### 5.2 Existing Systems
##### 5.2.1 Existing RL system & drawbacks(why it is not applicable for DM Transformer)
##### 5.2.2 Existing large model training system & drawbacks
#### 5.3 Explain decisions in resolving challengs(as in 5.1) for DM systems as a whole

### 7 Training System(Option B)
#### 5.1 Existing Systems
##### 5.2.1 Existing RL system
##### 5.2.2 Existing large model training system
#### 5.2 General challenges(reqirements and trends in model)
#### 5.3 Drawbacks of existing systems
#### 5.4 Explain decisions in resolving challengs(as in 5.1) for DM systems as a whole & summary

### 6 Conclusion

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