August 14, 2023
During the evaluation of machine learning (ML) models, the following question might arise: Is this…
Reinforcement learning in AI is a fascinating area of study that has garnered considerable interest in recent years. It simulates human-like learning processes to help the agent understand and adapt to its surroundings, making it a valuable tool in AI. Reinforcement learning has numerous applications in AI, including developing autonomous robots, self-driving cars, gaming, and more. Let’s learn more about reinforcement learning, how it works, and ways to implement it.
Reinforcement learning is a machine learning technique that enables an algorithm or agent to learn and improve its performance over time by receiving feedback as rewards or punishments. It is based on trial and error, where the agent learns by interacting with its environment and receiving feedback on its actions. The agent’s objective is to maximize its long-term rewards by taking the optimal course of action in each situation.
To achieve this objective, designers assign positive and negative values to desired and undesired behaviors. The agent then learns to avoid the negative and seek the positive, which trains it to make better decisions over time.
The reinforcement learning process can be broken down into the following steps, as illustrated in Figure-1:
Figure: Reinforcement learning block diagram
In reinforcement learning, the goal is not to explicitly tell the agent what actions to take in every situation but to allow it to learn from experience. Using trial and error, the agent can learn how to behave optimally in an environment, even in complex and dynamic situations.
The Bellman equation is a fundamental concept in reinforcement learning crucial in calculating the expected long-term rewards for each action the agent takes. The Bellman equation represents a recursive relationship between the expected reward of the current state-action pair and the expected reward of the next state-action pair. It is used to estimate the optimal action-value function, which is the expected long-term reward of taking a particular action in a specific state and following the optimal policy afterward.
The Bellman equation can be expressed as follows:
Q(s, a) = R(s, a) + γ * Σp(s’,r) * max Q(s’,a’)
The agent uses this equation to estimate the optimal Q-value for each state-action pair based on the current estimates and new experiences gained through interactions with the environment.
There are different ways to implement reinforcement learning, including value-based, policy-based, and model-based approaches.
Standard reinforcement learning algorithms include Q-learning, SARSA, and Deep Q-networks (DQN).
Q-learning is an algorithm that learns from random actions (greedy policy). It tries to find the next best action to maximize the reward randomly. The “Q” in Q-learning refers to the quality of activities throughout the process.
SARSA means State-Action-Reward-State-Action. Unlike Q-learning, the maximum reward for the next state is not necessarily used for updating the Q-values. Instead, a new action, and therefore reward, is selected using the same policy that determined the original action.
As the name suggests, DQN is Q-learning using neural networks. This algorithm combines neural networks with reinforcement learning techniques to achieve improved performance.
Reinforcement learning is an exciting study area with enormous potential for advancing AI technology. Its ability to simulate human-like learning processes opens up new possibilities for developing intelligent systems that can learn and adapt to their environments. Understanding the fundamentals of reinforcement learning and its applications can unlock new possibilities for building more innovative and efficient AI systems.
You can try training your own RL Agents using Comet’s integration with Gymnasium, a standard API for single-agent reinforcement learning environments.