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About Lecture Notes Deep Reinforcement Learning: Generalizability in Deep RL
The document provides information on deep reinforcement learning techniques including: 1) Deep Q-learning uses neural networks to approximate the Q-function and generalize to unseen states, since it is infeasible to estimate the Q-value of every single state-action pair. 2) A replay buffer stores experiences from agent-environment interactions to train the neural network on batches of randomly sampled experiences, making training more stable and efficient. 3) Training continues until the predicted Q-values converge closely with the expected Q-values, indicating the network has stabilized and learned an optimal policy.
Detailed Information
| Author: | ['Rajat Rai'] |
|---|---|
| Publication Year: | 2022 |
| Pages: | 7 |
| Language: | English |
| Format: | |
| Price: | FREE |
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