project management

README.md

@@ -21,12 +21,16 @@ I will be keeping around the logic for Q-learning on single action just for fina
 - [x] improvise decoder head variant, instead of concatenating previous actions at the frames + learned tokens stage. in other words, use classic encoder - decoder
     - [ ] allow for cross attention to fine frame / learned tokens
 
+
+- [ ] build out a simple dataset creator class, taking in the environment as an iterator / generator
+- [ ] redo maxvit with axial rotary embeddings + sigmoid gating for attending to nothing. enable flash attention for maxvit with this change
+
+- [ ] consult some RL experts and figure out if there are any new headways into resolving <a href="https://www.cs.toronto.edu/~cebly/Papers/CONQUR_ICML_2020_camera_ready.pdf">delusional bias</a>
+
 - [ ] for exploration, allow for finely randomizing a subset of actions, and not all actions at once
 - [ ] figure out if one can train with randomized orders of actions - order could be sent as a conditioning that is concatted or summed before attention layers
-- [ ] build out a simple dataset creator class, taking in the environment as an iterator / generator
+    - [ ] offer an improvised variant where the first action token suggests the action ordering. all actions aren't made equal, and some may need to attend to past actions more than others
 - [ ] see if the main idea in this paper is applicable to language models <a href="https://github.com/lucidrains/llama-qrlhf">here</a>
-- [ ] consult some RL experts and figure out if there are any new headways into resolving <a href="https://www.cs.toronto.edu/~cebly/Papers/CONQUR_ICML_2020_camera_ready.pdf">delusional bias</a>
-- [ ] redo maxvit with axial rotary embeddings + sigmoid gating for attending to nothing. enable flash attention for maxvit with this change
 
 ## Citations