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Learning How to Play Atari Games Through Deep Neural Networks
- Atari games like Pong can be framed as Reinforcement Learning (RL) problems, utilizing Markov Decision Processes.
- Tabular approaches face challenges due to the vast number of states in Atari games, leading to intractability.
- A shift to supervised learning poses issues due to the sequential nature of Atari games and the requirement for hand-labeled datasets.
- Deep-Q Networks (DQN) address Atari game challenges through function approximation and Q-learning.
- DQN uses Convolutional Neural Networks (CNNs) to handle continuous state spaces and distill image features.
- Function approximation in DQN involves approximating state-action values to generalize Q-values efficiently.
- Experience replay in DQN improves sample independence and addresses non-stationarity in data distribution.
- The introduction of a target network in DQN stabilizes training by reducing target instability.
- By stacking frames and pre-processing visuals, DQN ensures the Markovian property and enhances state representation.
- DQN's efficient training procedures leverage methods such as ε-greedy action selection and replay buffers for stable learning.
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