An agent operates in an environment where sequences of events unfold over time. The agent's behavior is described by a policy, denoted as π(a|s), which gives the probability of taking action 'a' when in state 's'. The environment's dynamics are described by a transition function, P(s'|s, a), which gives the probability of moving to the next state 's'' after taking action 'a' in state 's'. The process begins from an initial state, s₀, with a probability of P(s₀).
Consider the following specific
0
1
Tags
Ch.4 Alignment - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Analysis in Bloom's Taxonomy
Cognitive Psychology
Psychology
Social Science
Empirical Science
Science
Related
Objective Function as Expected Cumulative Reward (Performance Function)
An agent operates in an environment where sequences of events unfold over time. The agent's behavior is described by a policy, denoted as π(a|s), which gives the probability of taking action 'a' when in state 's'. The environment's dynamics are described by a transition function, P(s'|s, a), which gives the probability of moving to the next state 's'' after taking action 'a' in state 's'. The process begins from an initial state, s₀, with a probability of P(s₀).
Consider the following specific
Diagnosing a Faulty Sequence Generation Process
When modeling the generation of a sequence of states and actions as a Markov Decision Process, the probability of transitioning to a new state at any given step depends on the complete history of all states and actions that have occurred since the beginning of the sequence.
Notational Variations in State-Action Sequences (Trajectories)
An agent is generating a sequence by interacting with an environment. For a single time step, starting from state
s_t, arrange the following events in the correct logical order.