Reward Maximization

The framework of reinforcement learning outlines a system where agents enhance their performance by receiving feedback in the form of rewards and penalties from their environment. Typically, many approaches aim to optimize the total discounted reward, while various fields focus on maximizing the average reward per time step. These reward functions serve as normative representations of agent behaviors. For instance, in scenarios where objects blend into their surroundings, the senses of smell and taste become vital for survival.

Reward maximization entails identifying the policy that achieves the highest cumulative reward across all possible transition states within the Markov Decision Process (MDP). The policy emerges from the finite nature of the state set. The total cumulative reward can be calculated using a specific equation, where 't' indicates the policy level, 'g' is the discount factor, and 'm' denotes the number of transition steps. As the number of transitions increases, the reward diminishes relative to the distance in steps.

Researchers at DeepMind suggest a straightforward rule for developing AI and general intelligence, proposing that most intelligent capabilities stem from the overarching goal of maximizing rewards. However, they acknowledge that this theory is still in its early stages of validation. It will be intriguing to observe whether this concept proves successful and if it can be expanded into other realms, potentially making AI and general intelligence a reality.

The first video titled "Reinforcement Learning Explained in 90 Seconds" provides a quick overview of these concepts, making the basics of reinforcement learning accessible.

Exploitation

Reinforcement learning involves assessing possible actions and their outcomes to determine the most effective one. This learning framework incorporates both exploration and exploitation activities. Exploitation occurs when the agent applies knowledge gained from past experiences to its current context—often referred to as learning-by-doing. Generally, exploitation is more intricate than exploration. To illustrate, let’s consider two forms of reinforcement learning.

A memory system within a neural network comprises a DSP unit and a memory module for operations. Each partition of memory holds data for actions, state-value functions, and reward values. The operational memory contains a vector of agent IDs. The reinforcement learning agent retrieves Q-values and state-value functions from these memory modules and executes actions based on the information obtained.

Adaptation

Adaptation in reinforcement learning is a machine learning process where an agent learns to appropriately respond to rewards or penalties at any given time step. Parametric adaptation allows for precise adjustments to variations in tasks or restricted input movements. Typical applications include personalization and learning through demonstration. Additionally, combining Generative Adversarial Imitation Learning (GAIL) with reinforcement learning can enhance performance and accelerate the learning process.

The development of gene circuits capable of making decisions in specific environments represents a significant milestone towards the ambitious objective of creating living artificial intelligence. By integrating DNA with synthetic gene circuits, researchers can develop living systems that learn and adapt to new conditions. This combination lays the groundwork for mimicking neuromorphic behavior and addressing complex problems akin to those tackled by artificial neural networks. DNA and proteins, stored within cells, serve as both analog and digital memory.

The second video, "Reinforcement Learning: Machine Learning Meets Control Theory," elaborates on the intersection of these fields and their implications for modern AI development.