i am training my own DQN agent for a game i wrote in python, and the training loop works fine, but after about 150 episodes, cuda runs out of memory at optimize model function:
File “trainingAgent.py”, line 99, in 
optimize_model()
File “trainingAgent.py”, line 67, in optimize_model
next_state_values[non_final_mask] = target_net(non_final_next_states).max(1)[0].detach()
File “C:…\Local\Programs\Python\Python37\lib\site-packages\torch\nn\modules\module.py”, line 489, in call
result = self.forward(*input, **kwargs)
File “C:…\DQN.py”, line 28, in forward
x = F.relu(self.bn3(self.conv3(x)))
File “C:…\Python\Python37\lib\site-packages\torch\nn\functional.py”, line 862, in relu
result = torch.relu(input)
RuntimeError: CUDA out of memory. Tried to allocate 18.13 MiB (GPU 0; 8.00 GiB total capacity; 4.81 GiB already allocated; 17.54 MiB free; 1.43 GiB cached)
So yeah I have 8GB gpu memory, it is a gaming gpu. I’m not sure if this is normal but I guess not. Is it because my input screen matrix is too large? (300x300) cause i dont subtract the last screen from the current scren like in the example DQN algorithm (though i dont know if this lowers the input size?), but just use the current screen as the input for the network. Or is it some kind of memory leak? what could i do to solve the issue? I would like to be able to run at least 2000 episodes, and possibly 10,000 episodes if needed