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Reinforcement Learning Basics: RL Algorithms Explained

Category : Reinforcement Learning Basics | Sub Category : RL Algorithms Explained Posted on 2024-04-07 21:24:53


Reinforcement Learning Basics: RL Algorithms Explained

Reinforcement Learning Basics: RL Algorithms Explained

Introduction:

Reinforcement learning (RL) is a subfield of machine learning that focuses on coding intelligent agents to make decisions based on interactions with an environment. In RL, an agent learns to perform a series of actions in an environment to maximize a cumulative reward signal. RL algorithms play a vital role in enabling machines to learn from their experiences and make intelligent decisions. In this blog post, we will explore some of the most popular RL algorithms and discuss how they work.

1. Q-Learning:

Q-Learning is one of the fundamental RL algorithms that can handle environments with a finite number of states and actions. It uses a table to store the expected reward for each state-action pair, known as the Q-table. The agent explores the environment and updates the Q-values based on the Bellman equation, which incorporates the reward received and the estimated future rewards.

2. Deep Q-Network (DQN):

DQN is an extension of Q-Learning that leverages deep neural networks to handle environments with high-dimensional state spaces. It replaces the Q-table with a neural network that takes the current state as input and predicts the Q-values for each action. DQN uses experience replay to store and randomly sample past experiences, reducing the correlation between subsequent experiences and stabilizing the learning process.

3. Policy Gradient Methods:

Policy Gradient methods directly learn the optimal policy, which is a mapping from states to actions, without relying on a value function. These algorithms update the policy parameters in the direction of higher rewards through gradient ascent. The policy can be parameterized using a deep neural network, known as a policy network, which outputs a probability distribution over actions given a state.

4. Proximal Policy Optimization (PPO):

PPO is a popular policy gradient algorithm that addresses the instability issues associated with earlier methods. It uses a surrogate objective function to update the policy parameters while bounding the update to prevent drastic policy changes. PPO applies multiple policy updates per iteration, striking a balance between exploration and exploitation.

5. Actor-Critic Methods:

Actor-Critic methods combine the advantages of both policy-based and value-based methods. They maintain both a policy network and a value network, where the policy network guides action selection, while the value network estimates the expected cumulative rewards. The actor network updates the policy parameters based on the observed rewards, while the critic network updates the value parameters based on the TD (Temporal Difference) error.

Conclusion:

Reinforcement Learning algorithms enable machines to learn optimal actions through trial-and-error interactions with an environment. While this blog post only scratched the surface of RL algorithms, we discussed some significant ones, including Q-Learning, DQN, Policy Gradient methods, PPO, and Actor-Critic methods. Depending on the problem at hand, researchers and practitioners select the most appropriate algorithm to train RL agents effectively. As RL continues to advance, these algorithms form the building blocks for creating intelligent agents capable of making decisions in complex scenarios.

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