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Deep Reinforcement Learning

Deep Reinforcement Learning (DRL) is a subfield of RL that uses deep neural networks to solve more complex problems. In traditional RL, the states and actions are often represented in a simple table. However, this approach becomes impractical for problems with a vast number of states, such as a game of chess or a self-driving car navigating a city.

DRL Algorithms​

  • Deep Q-Networks (DQN): This algorithm combines Q-learning with a deep neural network to approximate the Q-values. It was famously used by DeepMind to master a wide range of Atari 2600 games, often surpassing human-level performance.

  • Policy Gradient Methods: These methods directly optimize the policy network. Examples include Proximal Policy Optimization (PPO) and Asynchronous Advantage Actor-Critic (A3C), which are known for their stability and performance in a variety of tasks, including robotics and game playing.

  • Actor-Critic Methods: These algorithms combine the strengths of both value-based and policy-based approaches. They have two components: an actor that learns the policy and a critic that learns the value function. This allows for more stable and efficient learning.

Benefits specific to DRL​

  • Handling Complexity: DRL's greatest strength is its ability to solve problems with incredibly complex and high-dimensional state and action spaces.

  • End-to-End Learning: DRL agents can learn directly from raw sensory input, such as images or sound, without the need for manual feature engineering. High-dimensional perception → action (vision-based control, end-to-end learning).

  • Generalization across large state spaces via shared network features.

  • Integrates with modern ML tooling (self-supervised pretraining, representation learning, offline RL).