I am in the process of implementing the DQN model from scratch in PyTorch with the target environment of Atari Pong. After a while of tweaking hyper-parameters, I cannot seem to get the model to achieve the performance that is reported in most publications (~ +21 reward; meaning that the agent wins almost every volley).
My most recent results are shown in the following figure. Note that the x axis is episodes (full games to 21), but the total training iterations is ~6.7 million.
The specifics of my setup are as follows:
class DQN(nn.Module): def __init__(self, in_channels, outputs): super(DQN, self).__init__() self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=32, kernel_size=8, stride=4) self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=4, stride=2) self.conv3 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1) self.fc1 = nn.Linear(in_features=64*7*7 , out_features=512) self.fc2 = nn.Linear(in_features=512, out_features=outputs) def forward(self, x): x = F.relu(self.conv1(x)) x = F.relu(self.conv2(x)) x = F.relu(self.conv3(x)) x = x.view(-1, 64 * 7 * 7) x = F.relu(self.fc1(x)) x = self.fc2(x) return x # return Q values of each action
- batch size: 32
- replay memory size: 100000
- initial epsilon: 1.0
- epsilon anneals linearly to 0.02 over 100000 steps
- random warmstart episodes: ~50000
- update target model every: 1000 steps
- optimizer = optim.RMSprop(policy_net.parameters(), lr=0.0025, alpha=0.9, eps=1e-02, momentum=0.0)
- OpenAI gym Pong-v0 environment
- Feeding model stacks of 4 last observed frames, scaled and cropped to 84x84 such that only the "playing area" is visible.
- Treat losing a volley (end-of-life) as a terminal state in the replay buffer.
- Using smooth_l1_loss, which acts as Huber loss
- Clipping gradients between -1 and 1 before optimizing
- I offset the beginning of each episode with 4-30 no-op steps as the papers suggest
Has anyone had a similar experience of getting stuck around 6 - 9 average reward per episode like this?
Any suggestions for changes to hyperparameters or algorithmic nuances would be greatly appreciated!