Comments (1)
There will still be a lot of CPU utilisation for various bits of code - for example, the environment does not run on GPU, and the replay memory is generally too large to be stored on GPU.
The codebase provides functionality for saving and loading models and memories (e.g. for use in offline RL), but does not provide "resume training" functionality. I won't be building this but PRs are welcome.
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Related Issues (20)
- Infinite loop in ReplayMemory._get_sample_from_segment HOT 1
- Noob Question : Need help running the code. It seems to be running for forever. HOT 1
- rainbow for multiagent setting HOT 1
- Policy and reward function HOT 4
- ploting the result HOT 1
- To run a demo HOT 2
- disable env.reset() after every episode HOT 5
- --learn-start HOT 2
- Delayed reward HOT 1
- load a model HOT 1
- Explanation of Q statistics in plots for "val_mem"? HOT 1
- [question] Training speed HOT 3
- Running Rainbow on a Cluster HOT 1
- IndexError: index 262141 is out of bounds for axis 0 with size 231071 HOT 2
- Evaluate the pretrained model HOT 1
- Stuck in memory._retrieve when batch size > 32 HOT 1
- A problem about one game in ALE cannot be trained HOT 1
- Montezuma's revenge - has this been tried using this codebase? HOT 2
- Data Effiecient Rainbow with Skiing does not work HOT 4
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