added summary

README.md

@@ -1,16 +1,12 @@
 # rl4fisheries
 
-Models:
-
-- `asm_env.py`: provides `AsmEnv()`. This encodes our population dynamics model, coupled with an observation process, and a harvest process with a corresponding utility model. These processes can all be modified using the `config` argument. Their defaults are defined in `asm_fns.py`. By default, observations are stock biomass and mean weight.
-- `asm_esc.py`: provides `AsmEscEnv()` which inherits from `AsmEnv` and has one difference to it: actions in `AsmEscEnv()` represent escapement levels rather than fishing intensities. 
-- `ams_cr_like.py`: provides `AsmCRLike()`. In this environment, mean weight is observed and the action is to set parameters `(x1, x2, y2)` for a biomass-based harvest control rule of the type `CautionaryRule` (specified below).
-
-Strategies evaluated with Bayesian Optimization: 
-
-- `agents.cautionary_rule.CautionaryRule`: piece-wise linear harvest-control rule specified by three parameters `(x1, x2, y2)`. Example plot (TBD).
-- `agents.msy.Msy`: constant mortality harvest control rule. Specified by one parameter `mortality`.
-- `agents.const_esc.ConstEsc`: constant escapement harvest control rule. Specified by one parameter `escapement`.
+RL and Bayesian optimization methodologies for harvest control rule optimization in fisheries.
+Includes:
+- A gymnasium environment for a Walleye population dynamics model
+- Policy functions for different commonly-tested policies (including those in the paper)
+- Scripts to optimize RL policies and non-RL policies
+- Notebooks to reproduce paper figures
+- Templates to train new RL policies on our Walleye environment
 
 ## Installation