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What is the difference between reinforcement learning and deep reinforcement learning?

What is the difference between reinforcement learning and deep reinforcement learning?

Artificial Intelligence & Machine Learning

Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) are closely related concepts, with DRL being a subset of RL. Here’s a breakdown of their differences:

Reinforcement Learning (RL):

  1. Definition: RL is a type of machine learning where an agent learns to behave in an environment by taking actions and receiving rewards, aiming to maximize some notion of cumulative reward.
  2. Model:
    • Can be model-based (where the agent tries to predict the outcome of its actions) or model-free (where the agent doesn’t try to predict outcomes but learns from experience).
  3. Function Approximation:
    • RL doesn’t specify the type of function approximator for policies or value functions. It can use simple linear models, decision trees, lookup tables, or any other kind of approximators.
  4. Applications:
    • Suitable for problems where the decision-making is sequential and the outcome is uncertain.
    • Used in game playing, robotics, optimization problems, and many other areas.
  5. Learning Process:
    • The agent interacts with the environment, receives feedback in the form of rewards or penalties, and updates its strategy (policy) to improve future rewards.

Deep Reinforcement Learning (DRL):

  1. Definition: DRL is an extension of RL where deep learning techniques, especially deep neural networks, are used to approximate the policy or the value function, or both.
  2. Model:
    • Like RL, DRL can also be model-based or model-free. Furthermore, the key difference is the use of deep neural networks as function approximators.
  3. Function Approximation:
    • DRL specifically uses deep neural networks to represent the policy or value function, especially useful when dealing with high-dimensional inputs like images or raw sensor data.
  4. Applications:
    • Has achieved state-of-the-art results in complex problems such as playing Atari games directly from pixel values, beating world champions in the game of Go, and mastering real-time strategy games like StarCraft II.
  5. Learning Process:
    • The process remains similar to traditional RL but involves the added complexity of training deep neural networks. Moreover, this often requires more data and computational power and introduces challenges like training stability and convergence.

Key Difference:

In conclusion, the main difference between RL and DRL is in the function approximation. While RL is a broad concept that doesn’t tie down the type of function approximator, DRL specifically uses deep neural networks as function approximators. Lastly, the incorporation of deep learning allows DRL to handle high-dimensional input spaces and complex function approximations that would be challenging or infeasible for traditional RL methods.

What is the difference between reinforcement learning and deep reinforcement learning? What is the difference between reinforcement learning and deep reinforcement learning?