DQN - Playing Atari with Deep Reinforcement Learning - RL Paper Explained

DQN - Playing Atari with Deep Reinforcement Learning - RL Paper Explained

Aleksa Gordić - The AI Epiphany via YouTube Direct link

The loss function explained

7 of 13

7 of 13

The loss function explained

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DQN - Playing Atari with Deep Reinforcement Learning - RL Paper Explained

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  1. 1 High-level overview of the paper
  2. 2 Experience replay buffer
  3. 3 Difficulties with RL correlations, non-stationary distributions
  4. 4 DQN is very general
  5. 5 MDP formalism and optimal Q function
  6. 6 Function approximators
  7. 7 The loss function explained
  8. 8 The deadly triad
  9. 9 Algorithm walk-through
  10. 10 Preprocessing and architecture details
  11. 11 Additional details - normalizing score, schedule, etc.
  12. 12 Agent training metrics
  13. 13 Results

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