Beginner Explanation
Imagine you’re learning to ride a bike. At first, you might wobble and fall, but each time you get back up, you remember what went wrong. You discover that leaning too far left makes you fall, so you adjust. This process of learning from your mistakes, understanding what works, and getting better each time is like a closed knowledge loop. It’s a cycle where each experience helps you improve your skills and understanding, just like how in a closed knowledge loop, discoveries, explanations, and generalizations help each other grow and improve continuously.Technical Explanation
A closed knowledge loop is a feedback mechanism in machine learning where the model iteratively improves its understanding of data through continuous cycles of discovery, explanation, and generalization. This can be implemented in reinforcement learning, where an agent learns optimal actions through trial and error, receiving feedback based on its performance. For example, consider a Q-learning algorithm: “`python import numpy as np class QLearningAgent: def __init__(self, actions, learning_rate=0.1, discount_factor=0.9): self.q_table = np.zeros((state_space, len(actions))) self.learning_rate = learning_rate self.discount_factor = discount_factor def update_q_value(self, state, action, reward, next_state): best_next_action = np.argmax(self.q_table[next_state]) td_target = reward + self.discount_factor * self.q_table[next_state][best_next_action] td_delta = td_target – self.q_table[state][action] self.q_table[state][action] += self.learning_rate * td_delta “` In this example, the agent continuously updates its Q-values based on the feedback received from the environment, creating a closed loop where its understanding of the environment improves over time.Academic Context
The concept of a closed knowledge loop is rooted in the principles of feedback systems and adaptive learning. In machine learning, this is closely associated with the theories of reinforcement learning, where agents learn through interactions with their environment. Key papers include Sutton and Barto’s ‘Reinforcement Learning: An Introduction’ which outlines the framework of Q-learning and Temporal Difference learning, emphasizing the iterative nature of learning and adaptation. Mathematically, the Bellman equation serves as a foundational component in defining the relationship between the current value of a state and the expected future rewards, illustrating the closed feedback loop in decision-making processes.Code Examples
Example 1:
import numpy as np
class QLearningAgent:
def __init__(self, actions, learning_rate=0.1, discount_factor=0.9):
self.q_table = np.zeros((state_space, len(actions)))
self.learning_rate = learning_rate
self.discount_factor = discount_factor
def update_q_value(self, state, action, reward, next_state):
best_next_action = np.argmax(self.q_table[next_state])
td_target = reward + self.discount_factor * self.q_table[next_state][best_next_action]
td_delta = td_target - self.q_table[state][action]
self.q_table[state][action] += self.learning_rate * td_deltaExample 2:
def __init__(self, actions, learning_rate=0.1, discount_factor=0.9):
self.q_table = np.zeros((state_space, len(actions)))
self.learning_rate = learning_rate
self.discount_factor = discount_factorExample 3:
def update_q_value(self, state, action, reward, next_state):
best_next_action = np.argmax(self.q_table[next_state])
td_target = reward + self.discount_factor * self.q_table[next_state][best_next_action]
td_delta = td_target - self.q_table[state][action]
self.q_table[state][action] += self.learning_rate * td_deltaExample 4:
import numpy as np
class QLearningAgent:
def __init__(self, actions, learning_rate=0.1, discount_factor=0.9):
self.q_table = np.zeros((state_space, len(actions)))Example 5:
class QLearningAgent:
def __init__(self, actions, learning_rate=0.1, discount_factor=0.9):
self.q_table = np.zeros((state_space, len(actions)))
self.learning_rate = learning_rate
self.discount_factor = discount_factorExample 6:
def __init__(self, actions, learning_rate=0.1, discount_factor=0.9):
self.q_table = np.zeros((state_space, len(actions)))
self.learning_rate = learning_rate
self.discount_factor = discount_factor
Example 7:
def update_q_value(self, state, action, reward, next_state):
best_next_action = np.argmax(self.q_table[next_state])
td_target = reward + self.discount_factor * self.q_table[next_state][best_next_action]
td_delta = td_target - self.q_table[state][action]
self.q_table[state][action] += self.learning_rate * td_deltaView Source: https://arxiv.org/abs/2511.16201v1