Basic Concepts in RL
- (Luzern, Switzerland - Alvin Wei-Cheng Wong)
- Overview
Reinforcement Learning (RL) is important because it allows an AI agent to learn by trial and error, essentially mimicking how humans and animals learn, by interacting with an environment and receiving rewards or penalties for its actions, enabling it to adapt and optimize strategies for complex, long-term goals in dynamic situations where immediate feedback isn't always available, unlike traditional machine learning (ML) methods that rely on pre-labeled data sets.
This makes it particularly useful for problems where decision-making needs to consider the consequences of actions over time, like robotics or game playing, where the agent can discover novel and creative solutions through exploration and experience.
Reinforcement learning (RL) is based on the Markov decision process (MDP), a mathematical modeling of decision-making that uses discrete time steps. At every step, the agent takes a new action that results in a new environment state. Similarly, the current state is attributed to the sequence of previous actions.
- The Markov Decision Process (MDP)
Reinforcement learning (RL) is currently undergoing rapid development in both methodologies and applications.
Although rooted in specialized algorithms developed by the computer science community, RL has grown into a field that deals with a wide range of methods for approximately solving intractable Markov decision processes, the fundamental model for sequential decision-making under uncertainty in operations research.
The Markov decision process (MDP) is a mathematical framework used for modeling decision-making problems where the outcomes are partly random and partly controllable. It's a framework that can address most RL problems.
RL is learning what to do given a situation and a set of possible actions from which to choose, in order to maximize reward. The learner, whom we call an agent, is not told what to do, he has to discover this for himself through interaction with the environment.
So RL is a set of methods that learn "how to (optimally) behave" in an environment, whereas MDP is a formal representation of such an environment.
- The Agent-Environment Interaction Process
In RL, the "Agent-Environment Interaction" process refers to the continuous cycle where an agent observes its current state within an environment, takes an action based on that state, receives a reward (or penalty) from the environment for that action, and then uses this feedback to update its internal strategy, ultimately aiming to learn the best possible actions to maximize long-term rewards within the given environment.
Key components of this process:
- Agent: The decision-making entity that interacts with the environment by choosing actions based on its current state and the received rewards.
- Environment: The external system that the agent interacts with, providing information about the current state and responding to the agent's actions with rewards or penalties.
- State: A specific situation or configuration within the environment that the agent observes.
- Action: A decision made by the agent that can change the state of the environment.
- Reward: Feedback from the environment indicating how well the agent's action performed in a given state.
How the interaction works:
- Observe State: The agent perceives the current state of the environment.
- Select Action: Based on the observed state, the agent chooses an action to take.
- Execute Action: The agent performs the chosen action in the environment.
- Receive Reward: The environment provides a reward (positive or negative) to the agent based on the action taken.
- Update Policy: The agent uses the received reward to update its internal strategy (policy) for future decision-making, aiming to maximize long-term rewards.
For example,
A robot learning to navigate a maze:
- State: The robot's current location in the maze.
- Action: Moving forward, turning left, turning right.
- Reward: Positive reward for reaching the exit, negative reward for hitting a wall.
- The Reward Maximization Process
In RL, "Reward Maximization" refers to the core process where an agent, interacting with an environment, learns to take actions that consistently produce the highest possible cumulative reward over time, essentially optimizing its behavior by choosing actions that lead to the most beneficial outcomes as defined by the reward function set by the designer.
Key characteristics about Reward Maximization:
- Agent and Environment: An agent within a reinforcement learning system interacts with an environment, receiving feedback in the form of rewards (positive or negative) for its actions.
- Trial and Error: The agent learns through trial and error, trying different actions in different states and observing the resulting rewards, allowing it to gradually refine its strategy to maximize the overall reward.
- Reward Function: A crucial element, the reward function defines what constitutes a "good" action by assigning positive rewards to desired behaviors and negative rewards to undesirable ones.
- Policy Improvement: Based on the received rewards, the agent updates its internal policy (decision-making strategy) to choose actions that are more likely to lead to higher future rewards.
How Reward Maximization works:
- Observe State: The agent perceives the current state of the environment.
- Take Action: The agent selects an action based on its current policy and the observed state.
- Receive Reward: The environment provides a reward signal based on the chosen action.
- Update Policy: Using the received reward, the agent updates its policy to favor actions that led to higher rewards in similar situations.
For Example,
- Robot Learning to Walk: A robot learning to walk might be rewarded for taking steps forward and penalized for falling over. Over time, the robot would learn to adjust its movements to maximize the positive rewards and minimize the negative ones, leading to better walking ability.
- The Trial-and-Error Learning Process
Reinforcement learning (RL) is a ML technique that trains software to make decisions to achieve optimal results. It is based on rewarding desired behavior and punishing undesirable behavior.
In RL, an agent learns how to behave in its environment by performing operations and viewing the results. For every good behavior, the agent will receive positive feedback, and for every bad behavior, the agent will receive negative feedback or punishment.
RL mimics the trial-and-error learning process humans use to achieve goals. For example, you can use a reward system to train your dog. When the dog behaves well, you reward it; when it does something wrong, you punish it.
Various software and machines use RL to find the best behavior or path to take in a given situation. Some examples of RL include: predictive text, text summarization, question answering, machine translation.
Some challenges and limitations of reinforcement learning include:
- High-dimensional and continuous state and action space
- Noisy and incomplete data
- Dynamic and adversarial environments
[More to come ...]
