- Can GPT-4 play a good game of rock paper scissors?
- could set up this experiment up through the OpenAI API
- Can neuro-evolution lead to something that surpasses Iocaine Powder, in the sense that it is a very good general strategy?
- Can neuro-evolution attain the level of Iocaine Powder purely through self-play?
- If so, how large/complex would it need to be?
- Is there an upper bound of complexity for RPS strategies?
- Assuming not, would the evaluation period need to grow exponentially?
- Also, if not, could RPS (or other generic task) act to "train" or "grow" a foundation model for time series in general?
- Other than asymmetric rewards, are there ways of avoiding simplistic local minima?
- Is there any relationship between RPS and compression algorithms?
- Could this be a useful medium/input?
- If we let evolution go on for a looooong time, do we ever observe things like double descent or grokking take place with generalization?
- How well would a purely supervised learner do at this?
- Does CMA-ES outperform SNES on this task?
- "Hall of champions" for improved self-play and generality.
- More model types
- Transformers
- LSM/Echo State Networks?
- Larger models
- More hidden layers
- Larger hidden layers
- RL algorithms / online learning
- Parallelization
- helper function to setup Ray cluster on, say, AWS
- Hyperparameter search
- EvoTorch, Optuna, ...?
- Named experiments, better config handling
- SQLite?
- Integration and unit tests
- Web app leaderboard
- GPU support
- (current RNN models are too small for GPUs to help much)
- More visualizations
- Moving average of various n-grams through time used by agents