Reinforcement learning is an artificial intelligence (AI) technique where an agent learns to take actions in an environment to maximize a reward signal. One of the challenges in reinforcement learning is exploring the environment to find the best actions to take while also exploiting the knowledge the agent already has. This is called the exploration-exploitation tradeoff. Too much exploration and the agent might not find the best actions to take. Too much exploitation and the agent might get stuck in a suboptimal policy.
What is Epsilon Greedy Exploration?
Epsilon Greedy Exploration is an exploration strategy in reinforcement learning. It takes a greedy action with probability $1-\epsilon$ and a random exploratory action with probability $\epsilon$. This means that the agent will sometimes choose the best action it knows with a high probability and sometimes explore new actions it hasn't tried before with a low probability.
The value of $\epsilon$ is between 0 and 1. If $\epsilon$ is 0, the agent will always take the greedy action, which means it will exploit the knowledge it has acquired so far. If $\epsilon$ is 1, the agent will always take a random exploratory action, which means it will explore the environment without any regards to the knowledge it has acquired so far.
Epsilon Greedy Exploration is a simple and effective strategy to balance exploration and exploitation. The agent is able to explore the environment without ignoring its current knowledge. The value of $\epsilon$ can be adjusted during the training process to fine-tune the exploration-exploitation tradeoff.
How Does Epsilon Greedy Exploration Work?
Epsilon Greedy Exploration works by blending the greedy policy and a random exploratory policy. The agent is able to make decisions based on its current knowledge while also exploring unknown actions.
At the beginning of the training process, the agent doesn’t have any knowledge about the environment. In this case, $\epsilon$ is set to 1, which means the agent will take random exploratory actions. As the agent learns more about the environment, $\epsilon$ is gradually decreased to increase the chance of exploitation.
For example, let’s consider a simple grid world environment where an agent needs to go from the starting point to the goal point without hitting any obstacles. The agent is equipped with four actions: up, down, left and right. The agent can only move one cell at a time. At the beginning of the training process, the agent doesn’t have any knowledge about the environment, so $\epsilon$ is set to 1. The agent chooses an action randomly with a probability of 0.25. After taking some steps, the agent starts to learn about the environment. The agent knows some good moves that lead it closer to the goal. In this case, it makes sense to exploit the agent's knowledge by setting $\epsilon$ to 0.1. The agent chooses the best move with a high probability and explores other moves with a low probability.
Epsilon Greedy Exploration is a widely used exploration strategy in reinforcement learning because it’s simple, easy to implement, and works well in a variety of environments.
Applications of Epsilon Greedy Exploration
Epsilon Greedy Exploration is used in several state-of-the-art reinforcement learning models. It’s widely used in Q-learning, a popular algorithm for learning optimal policies in Markov Decision Processes (MDPs).
MDPs are mathematical models that capture the decision-making process in a sequential environment where the outcomes are partly random and partly controlled by the decision-maker. A famous example of an MDP is a game of chess. The state of the game changes as the players take turns making moves. The outcome of each move is partly random (due to the uncertainty of the opponent’s next move) and partly controlled by the player.
In Q-learning, the agent learns to take optimal actions by estimating the value of taking a specific action in a given state. The value function is updated using the Bellman equation. Epsilon Greedy Exploration is used as a behaviour policy during the training process to balance exploration and exploitation. The agent takes a greedy action with probability $1-\epsilon$ and a random exploratory action with probability $\epsilon$.
Epsilon Greedy Exploration is also used in Deep Q-Networks (DQNs), a state-of-the-art algorithm for learning optimal policies in high-dimensional environments. DQNs use a neural network to approximate the optimal action-value function. Epsilon Greedy Exploration is used to balance exploration and exploitation in the high-dimensional action space.
Epsilon Greedy Exploration is a simple and effective way to balance exploration and exploitation in reinforcement learning. It works by taking a greedy action with a high probability and a random exploratory action with a low probability. The value of $\epsilon$ can be adjusted during the training process to fine-tune the exploration-exploitation tradeoff. Epsilon Greedy Exploration is widely used in Q-learning and Deep Q-Networks, two state-of-the-art algorithms for learning optimal policies in MDPs.
