Implement a reinforcement learning model which can play Tetris
| agent.py | ||
| enviroment.py | ||
| game.py | ||
| network.py | ||
| README.md | ||
| train.py | ||
TetrisRL
- Implement a reinforcement learning model which can play Tetris
Tetris Enviroment
- 10 actions
- 0: don't move
- 1: shift left 1 block
- 2: shift left 2 block
- 3: shift left 3 block
- 4: shift right 1 block
- 5: shift right 2 block
- 6: shift right 3 block
- 7: rotate once
- 8: rotate twice
- 9: rotate three times
- return
- pixel(10*20)
- block_id
- block_location(x, y)
Version 0
- Policy Gradient Algorithm
- Reward Delay
- Bad Performance
TODO
- change reward function
- reward baseline
- DQN