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# Dueling DDQN with Prioritized Experience Replay
Each experiment uses 3 seeds and is trained for 10k environment steps.
The parameters used for Dueling DDQN with PER are the same parameters as described in the [following paper](https://arxiv.org/abs/1511.05952).
### Breakout Dueling DDQN with PER - single worker
```bash
python3 coach.py -p Atari_Dueling_DDQN_with_PER_OpenAI -lvl breakout
```
<img src="breakout_dueling_ddqn_with_per.png" alt="Breakout Dueling DDQN with PER" width="800"/>
### Pong Dueling DDQN with PER - single worker
```bash
python3 coach.py -p Atari_Dueling_DDQN_with_PER_OpenAI -lvl pong
```
<img src="pong_dueling_ddqn_with_per.png" alt="Pong Dueling DDQN with PER" width="800"/>
### Space Invaders Dueling DDQN with PER - single worker
```bash
python3 coach.py -p Atari_Dueling_DDQN_with_PER_OpenAI -lvl space_invaders
```
<img src="space_invaders_dueling_ddqn_with_per.png" alt="Space Invaders Dueling DDQN with PER" width="800"/>