Question

Which among the following statements provides the difference between reinforcement-based learning and temporal difference technique?

(a) State represented by a directed graph

(b) Assignment of weightage to an action on the basis of the degree of success

(c) Computation of degree of success

(d) Priori model of the sequence of possible states

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My enquiry is from Next Generation Wireless Network in division Next Generation Wireless Network of Cognitive Radio

Answer 1

The correct answer is:

(d) Priori model of the sequence of possible states

Explanation:

The distinction between reinforcement-based learning and temporal difference (TD) learning lies primarily in how the degree of success (or reward) is evaluated and used for learning.

• Reinforcement-based learning typically involves receiving a reward after completing an entire episode or sequence of actions, and the agent makes updates based on the total outcome after the task is finished.

• Temporal Difference (TD) learning, on the other hand, does not require the agent to wait until the end of the episode to update its knowledge. It updates estimates based on the observed rewards and the expected rewards for the next state (the value of the next state), and it is a type of model-free learning.

A priori model of the sequence of possible states (as mentioned in option (d)) is a key feature of some learning methods, especially when planning or predictions are involved. TD learning does not require a prior model of the sequence of states, but rather updates state values on the basis of observed transitions and rewards. Therefore, this is a distinguishing factor between reinforcement learning in general (which might assume a model of the environment) and temporal difference methods (which operate without an explicit model of the state transitions).