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About Playing Tetris With Deep Reinforcement Learning: Matt Stevens Sabeek Pradhan
This paper explores using deep reinforcement learning to train an AI agent to play Tetris. The authors use a convolutional neural network to estimate action values and take the best actions at each game state. Several techniques were used to improve learning, including grouping actions, transfer learning from a heuristic model, and prioritized sweeping. The agent was able to learn to play Tetris effectively, and performance could be further improved with more training.
Detailed Information
| Author: | ['Peter'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 7 |
| Language: | English |
| Format: | |
| Price: | FREE |
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