Deep reinforcement learning (DRL) differs from traditional reinforcement learning (RL) primarily in the way it represents and learns the state-action value function or policy. Traditional RL often relies on handcrafted features or simple tabular representations, which can be limiting in complex environments with high-dimensional state spaces.
DRL integrates deep learning techniques, typically using deep neural networks to approximate the value function or policy. This allows DRL agents to learn directly from raw sensory inputs, such as images or sound, without the need for manual feature engineering. The deep neural networks in DRL can capture complex patterns and representations, enabling the agent to handle more intricate tasks.
For example, in a game like Go, the state space is incredibly complex. Traditional RL methods would struggle to represent this space effectively. However, a DRL agent, like AlphaGo, uses a deep neural network to evaluate board positions and select moves, demonstrating superior performance over traditional approaches.
In the context of cloud computing, services like Tencent Cloud offer powerful GPU instances that are well-suited for training deep reinforcement learning models. These instances provide the necessary computational resources to handle the large-scale matrix operations required for deep learning, making them ideal for DRL tasks.