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Deep Reinforcement Learning using python

Deep Reinforcement Learning using python

Reinforcement learning  concerned with creating intelligent robots which is a sub-field of machine learning that achieved impressive results in the recent years where now we can build robots that can beat humans in very hard  games like alpha-go game and chess game.

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Deep Reinforcement Learning (DRL) is a subfield of machine learning that combines reinforcement learning with deep neural networks. It allows the learning agent to take advantage of the powerful function approximation capabilities of deep neural networks to learn complex policies or value functions.

In Python, several libraries such as TensorFlow, Keras, PyTorch and Stable Baselines provide implementations of various deep reinforcement learning algorithms. These libraries provide pre-built neural network architectures and optimizers that can be used to train the agent.

One popular algorithm in DRL is called Deep Q-Network (DQN). DQN is an extension of Q-learning, a traditional reinforcement learning algorithm, that uses a deep neural network to approximate the Q-value function. The agent learns to take actions that maximize the expected cumulative reward by adjusting the parameters of the neural network.

Another popular algorithm is the Proximal Policy Optimization (PPO) algorithm. PPO is an on-policy algorithm that uses a neural network to represent the policy (mapping from states to actions) and optimize the policy parameters directly. It uses a trust region method to stabilize the training and make it more sample efficient.

It's important to note that DRL is a computationally intensive task and it requires a lot of data and computational resources. It can be used to solve a wide variety of problems such as game playing, robotic control, and decision making in autonomous systems. Additionally, it's important to be aware of the different hyperparameters and techniques that can be used to improve the performance of the algorithms and selecting the appropriate one for the specific problem.

What you'll learn

  • Understand deep reinforcement learning and its applications
  • Build your own neural network
  • Implement 5 different reinforcement learning projects
  • Learn a lot of ways to improve your robot
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