This is a simplistic implementation of DQN that works under CartPole-v0 with rendered pixels as input. It extends the implementation of pytorch's official DQN tutorial (which doesn't actually work) https://pytorch.org/tutorials/intermediate/reinforcement_q_learning.html.
It's tuned specifically for CartPole-v0 and will possibly fail for other tasks.
- pytorch 1.0+
- torchvision
- OpenAI gym
- matplotlib
- Pillow
Due to the randomness of the DQN algorithm (random training samples & random network initialization), the training is not deterministic. You may have to restart a few times to get a satisfying result.
The following 3 experiment results came from the same set of hyper parameters:
| Trial | Max reward | Max 100-mean | Total episodes | Solved after (episodes) |
|---|---|---|---|---|
| 0 | 1600 | 220 | 5000 | 3000 |
| 1 | 900 | 160 | 5000 | - |
| 2 | 2500 | 500 | 10000 | 700 |
The last column indicates when the 100-mean reached 200.
Last 300 episode history of training:
- Blue curve: episode reward
- Red curve: episode loss
- Orange curve: 100-mean
- Higher green line: max reward
- Lower green line: max 100-mean
- Preprocess:
- stack last 3 frames
- grayscale, crop, resize
- Net work:
- conv: 4 layers with a kernel size of 5 and stride of 2
- flatten: simply reshape all pixels from conv output
- output: fully connect flattened features to produce 1 output for Value and 2 outputs for Advantage
- Double DQN
- Duelling DQN
- Prioritized replay memory
- Adam optimizer
- MSE loss
- Separate render thread
| Parameter | Value |
|---|---|
| Learning rate | 3e-5 |
| Target net update (steps) | 200 |
| Batch size | 256 |
| Gamma | 1 |
| Memory size | 10000 |
| Memory alpha | 0.6 |
| Memory beta start | 0.4 |
| Memory beta frames | 10000 |
| eps start | 1.0 |
| eps end | 0.01 |
| eps decay | 10 |
- The training loss never converges, while performance keeps improving.
- Training is very sensitive to hyperparameter changes.
- Although the 100-mean is quite high and stable after some training, the network may still perform some terrible moves.
- It's quite sensitive to initialization, which can only be controlled with a random seed.
- High rate of random sample rate (epsilon) seems to ruin the training. Only a small one works.