Update README.md

CodeElements/EvalCT/README.md

@@ -7,15 +7,19 @@ First, make sure you are under `CodeElements/EvalCT/`. To run `eval_ct.py`, we n
 svn export -r 90 --force https://github.com/google-research/circuit_training.git/trunk/circuit_training
 ```
 
-Next, we need to prepare your trained policy and the testcase you want to evaluate on. Assume you have trained models (which usually can be found under `./logs`), copy the run folder into `saved_model` folder. Make sure your testcase is under `./test`.
+Next, we need to prepare your trained policy and the testcase you want to evaluate on. Assume you have trained models (which usually can be found under `./logs`), copy the run folder into `saved_model` folder. Make sure your testcase is under `./test`. The `ckptID` is the policy checkpoint ID saved after each iteration.
 
 Finally, run the following command with path to netlist file, initial placement file and model run directory path.
 ```
 $ python3 -m eval_ct --netlist ./test/ariane/netlist.pb.txt\
             --plc ./test/ariane/initial.plc\
-            --rundir run_os_64128_g657_ub5_nruns10_c5_r3_v3_rc1
+            --rundir run_00\
+            --ckptID policy_checkpoint_0000103984
 ```
 The placement will be stored under `CodeElements/EvalCT/` and named as `eval_[RUN_DIR]_to_[TESTCASE].plc`.
 
+## Trained Policy
+We are providing one of the run we trained from scratch using Google's Ariane testcase. **This is not a truthful representation of the potential of Circuit Training**. We are only providing these trained weights here for the sake of testing. Please feel free to load any of your own trained weights. You may find similar file structure under `./logs` after training.
+
 ## View Your Result
 You can view the result by supplying this placement file into the open-sourced Plc_client testbench and use the `display_canvas` function.