Deep Learning vs. Reinforcement Learning
Deep learning analyses a training set, identifies complex patterns and applies them to new data.
Reinforcement learning works sequentially in an unknown environment━taking an action, evaluating the rewards, and adjusting the following actions accordingly.
Deep learning and reinforcement learning complement each other:
- Reinforcement learning algorithms manage the sequential process of taking an action, evaluating the result, and selecting the next best action. However, they need a good mechanism to select the best action based on previous interactions.
- Deep learning can be that mechanism━it is the most powerful method available today to learn the best outcome based on previous data.
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