In terms of design and implementation, a reinforcement learning (RL) framework is an organized approach that can be used to create RL algorithms. It is a combination of various components that ultimately allow an agent to learn and be able to make the best decisions within its surroundings.
In an RL framework, the core components are:
1. Agent: The agent represents the decision-making entity that interacts with the environment. It can be a physical robot, a software program, or even a human. The agent’s goal is to learn how to take actions that maximize its cumulative reward over time.
2. Environment: The environment represents the world in which the agent operates. It provides the agent with feedback in the form of rewards and penalties, and it defines the constraints and limitations of the agent’s actions. The environment can be physical, simulated, or even abstract.
3. Policy: The policy represents the agent’s strategy for selecting actions in a given state. It is a function that maps from states to actions. The agent’s goal is to learn a policy that maximizes its expected cumulative reward.
4. Reward Function: The reward function defines the immediate feedback that the agent receives for taking a particular action. It assigns a numerical value to each state-action pair, indicating the desirability of that action in that state. The reward function plays a crucial role in shaping the agent’s behavior.
5. Value Function: The value function represents the long-term expected reward that the agent can accumulate from a given state. It is a function that maps from states to values. The value function provides guidance to the agent for selecting actions that lead to the highest expected future rewards.
Reinforcement learning algorithm:
The RL algorithm is the core computational procedure that drives the learning process. It iteratively updates the agent’s policy based on the feedback it receives from the environment.
The algorithm typically involves the following steps:
1. Initialization: Initialize the agent’s policy and value function.
2. Sample State-Action Pair: Sample a state-action pair from the environment.
3. Execute Action: Execute the selected action and observe the resulting state and reward.
4. Update Policy: Update the agent’s policy based on the experienced state-action-reward transition.
5. Update Value Function: Update the agent’s value function based on the experienced state-action-reward transition.
6. Repeat: Repeat steps 2-5 until the agent’s performance converges or the learning process terminates.