Price: $38.99 - $33.01
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Publisher ‏ : ‎ Packt Publishing (October 18, 2019)
Language ‏ : ‎ English
Paperback ‏ : ‎ 366 pages
ISBN-10 ‏ : ‎ 1789131111
ISBN-13 ‏ : ‎ 978-1789131116
Item Weight ‏ : ‎ 1.41 pounds
Dimensions ‏ : ‎ 9.25 x 7.52 x 0.76 inches

Reinforcement Learning Algorithms with Python

Reinforcement learning is a powerful machine learning technique that enables an agent to learn how to make decisions by interacting with an environment. In this post, we will explore some popular reinforcement learning algorithms implemented in Python.

1. Q-Learning: Q-learning is a model-free reinforcement learning algorithm that aims to learn the optimal policy for an agent to take actions in an environment. It works by updating the Q-values of state-action pairs based on the rewards received from the environment.

2. Deep Q-Network (DQN): DQN is an extension of Q-learning that uses a deep neural network to approximate the Q-values. This allows the algorithm to handle high-dimensional state spaces and achieve better performance on complex tasks.

3. Policy Gradient Methods: Policy gradient methods directly learn the policy function that maps states to actions. They use gradient ascent to update the parameters of the policy in order to maximize the expected return.

4. Actor-Critic Methods: Actor-critic methods combine the advantages of policy gradient methods and value-based methods. They use an actor network to learn the policy and a critic network to estimate the value function.

To implement these reinforcement learning algorithms in Python, you can use popular libraries such as TensorFlow, PyTorch, or OpenAI Gym. These libraries provide the necessary tools and environments to train and evaluate your reinforcement learning models.

Overall, reinforcement learning algorithms offer a powerful framework for training intelligent agents to solve complex decision-making tasks. By using Python, you can easily implement and experiment with these algorithms to build cutting-edge AI systems.
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