Learn Before
Causality Constraint in Reinforcement Learning
In reinforcement learning, policy decisions operate under a causality constraint. This means that an action selected at a specific time step, , can only impact rewards obtained at or after that time (). Rewards received prior to time are considered unchangeable or 'fixed' from the perspective of the action at .
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Ch.4 Alignment - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Related
Theory
Concept
Misinformation
Information Overload
Prototypes
General Knowledge References
Information References
Literacy
The Three Forms of Information
Information Disciplines
Information Dissemination
Distributed Summation Implementation
Vector Transformation Formula
Matrix Bracket Notation
Query, Key, and Value in Attention Mechanisms
Average Value Notation ()
Function of a Predicted Future Value Notation ()
Index Calculation for Sequence Start Position
Sequence of Cyclic Subgroups Notation
Conditional Probability of the Next Element in a Sequence
Data vs. Information in Model Training
A climate scientist reads ten peer-reviewed articles, synthesizes the data and arguments presented, and develops a new, deeper understanding of the acceleration of glacial melt. This new understanding within the scientist's mind best exemplifies which of the following?
Start Index Calculation for a Context Window
Vector Prefix Notation
A user asks a large language model to explain a scientific concept. The model retrieves relevant data, synthesizes it, and generates a paragraph as a response. The user reads this paragraph and gains a new understanding. Which part of this scenario best exemplifies 'information-as-process'?
Uncluttered Notation for Encoder-Classifier Models
Data (Information)
Causality Constraint in Reinforcement Learning
Draft Model Probability Distribution ()
Weight Matrix Definition ()
Predicted Future Value Notation ()
Policy in Reinforcement Learning ()
Sequence of Elements in Angle Brackets Notation
Learn After
Irrelevance of Past Rewards for Policy Gradient Calculation
An autonomous agent completes a task over four time steps. The sequence of actions and resulting rewards is as follows:
- Time t=1: Action
a_1-> Rewardr_1 = 0 - Time t=2: Action
a_2-> Rewardr_2 = 0 - Time t=3: Action
a_3-> Rewardr_3 = -1 - Time t=4: Action
a_4-> Rewardr_4 = +10
When evaluating the decision to take action
a_2at time t=2, which rewards should be considered as being potentially influenced by this specific action?- Time t=1: Action
An agent is learning to play a video game. At time step
t=5, the agent performs an action (e.g., jumping). According to the causality principle in this context, this specific action att=5can alter the reward that was already received at time stept=3.Causality Principle in Policy Gradient Calculation
Debugging a Policy Update Calculation