deep reinforcement learning
Deep reinforcement learning is an area within artificial intelligence that combines deep learning techniques with reinforcement learning to enable autonomous agents to learn and adapt to complex tasks and environments. It involves the use of deep neural networks to process high-dimensional input data and make decisions based on rewards received from the environment, allowing the agent to learn through trial and error.
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Related Concepts (2)
Similar Concepts
- adaptive learning
- adversarial reinforcement learning
- experimental learning
- incremental learning
- inverse reinforcement learning
- iterative learning
- positive reinforcement
- quantum reinforcement learning
- reinforcement learning
- reinforcement learning with attention
- reinforcement schedules
- reinforcement theory
- reinforcing feedback loops
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