Repository containing Chess Engine and ML Model
- compare separated vs non-separated network
- get gpu acceleration
- tweak rewards (inputs) to see best one-prioritize defense
- custom loss function (outputs) to capture more chess strategy
- GradientTapes w/ custom training loop
- Set up RNN/cnnRNN,CNN/Greedy/Greedy+exploit
- Move w.r.t. various reward functions
- Set up qlearning (links game to models>)
- Train/Test
- IReinfLearn to learn reward
- Submit to Lichess
- unit tests for board and game
- Write actual integration tests (one for why moves needs to be updated after each move)
- i think it only needs tobe updated before a king moves
- write 2 system tests
- implement scoreboard + visualization
- db contains
players=(ID,name) andscores=(ID,ID, score)
- db contains
- type documentation + doc strings
- optimizations
- change coordinate system to 2-digit int
- remove
if x.is_white: ... else: ...by inverting board at start/end of code
- write AI - X-d,All-b=2s thinking time
- Start frontend
- Write Controller to interact Model and View
- Write Buttons for View
- Lose to AI