Flexibility and Abstraction in the framework

The flexibility and abstraction in the reinforcement learning framework enable it to be applied to various problems and contexts such as time steps, action types, and state representation.

Time steps: Flexible interpretation of time steps can be found in reinforcement learning since it doesn’t need to correspond to fixed-time intervals. Instead they can represent any sequence of decision-making stages.
Actions: The actions can range from low-level controls to high-level decisions. For instance, the voltages applied to the motors of a robot arm represent the low-level controls. Conversely, high-decision decisions could include choices like whether to pursue a graduate degree or what to have for lunch. These diverse actions demonstrate the flexibility of the reinforcement learning framework in handling various types of decision-making processes.
States: Similar to actions, states can also be represented in several ways, from low-level sensor readings to high-level abstract descriptions. For example, direct sensor reading represents the low-level sensations and the symbolic representation of objects in a room represents the high-level abstractions.

Agent-Environment Interface in AI

The agent-environment interface is a fundamental concept of reinforcement learning. It encapsulates the continuous interaction between an autonomous agent and its surrounding environment that forms the basis of how the agents learn from and adapt to their experiences to achieve specific goals. This article explores the decision-making process of agents, the flexibility of the framework, and the critical distinction between the agent and its environment.

Table of Content

Agent-environment Interface in AI

Time steps and continual interaction
Perception, Action, and Feedback
Representation of State, Action and Rewards

Policy and Decision-making

Policy
Decision making

Finite Markov Decision process

Components of Finite MDP
Dynamics of Finite MDP