UPDATE: initial states in episode_start and in cartpole-updated-simparam.yaml

initial states should be defined before episode_step. Previously these were randomly initialized as standard gaussians in episode_start using the keys defined by simulator.states. Now a new value is provdied in simulator.yaml called initial_states which takes a dictionary of initial_values. Moreover, episode_start looks in the `config` Dict (for instance when using scenario parameters in Inkling from lessons) and updates initial_states if provided.

TODO: update documentation and all simulator.yaml files

conf/config.yaml

@@ -1,4 +1,4 @@
 defaults:
   - data: cartpole-updated.yaml
-  - model: SVR.yaml
+  - model: xgboost.yaml
   - simulator: cartpole-updated-simparam.yaml

conf/simulator/cartpole-updated-simparam.yaml

@@ -6,6 +6,16 @@ simulator:
   # estimate these during training
   # e.g.,:
   episode_inits: { "pole_length": 0.4, "pole_mass": 0.055, "cart_mass": 0.31 }
+  # e.g.,:  your simulator may need to know the initial state
+  # before the first episode. define these here as a dictionary
+  # you can include these in your Inkling scenarios during brain training
+  initial_states:
+    {
+      "cart_position": 0,
+      "cart_velocity": 0,
+      "pole_angle": 0,
+      "pole_angular_velocity": 0,
+    }
   # episode_inits:
   policy: bonsai
   logging: enable

ddm_predictor.py

@@ -44,6 +44,7 @@ class Simulator(BaseModel):
         inputs: List[str],
         outputs: List[str],
         episode_inits: Dict[str, float],
+        initial_states: Dict[str, float],
         diff_state: bool = False,
     ):
 
@@ -57,14 +58,30 @@ class Simulator(BaseModel):
         self.state_keys = states
         self.action_keys = actions
         self.diff_state = diff_state
+        self.initial_states = initial_states
         # TODO: Add logging
 
         logger.info(f"DDM features: {self.features}")
         logger.info(f"DDM outputs: {self.labels}")
 
     def episode_start(self, config: Dict[str, Any] = None):
+        """Initial DDM with initial states. This could include initializations of configs
+        as well as initial values for actions
 
-        initial_state = {k: random.random() for k in self.state_keys}
+        Parameters
+        ----------
+        config : Dict[str, Any], optional
+            episode initializations, by default None
+        """
+
+        # initialize states based on simulator.yaml
+        initial_state = self.initial_states
+        # define initial state from config if available (e.g. when brain training)
+        # skip if config missing
+        if config:
+            initial_state.update(
+                (k, config[k]) for k in initial_state.keys() & config.keys()
+            )
         initial_action = {k: random.random() for k in self.action_keys}
         if config:
             logger.info(f"Initializing episode with provided config: {config}")
@@ -82,6 +99,7 @@ class Simulator(BaseModel):
             # request_continue = input("Are you sure you want to continue with random configs?")
             self.config = {k: random.random() for k in self.config_keys}
         self.state = initial_state
+        logger.info(f"Initial states: {initial_state}")
         self.action = initial_action
         # capture all data
         # TODO: check if we can pick a subset of data yaml, i.e., what happens if
@@ -206,6 +224,7 @@ def main(cfg: DictConfig):
     states = cfg["simulator"]["states"]
     actions = cfg["simulator"]["actions"]
     configs = cfg["simulator"]["configs"]
+    initial_states = cfg["simulator"]["initial_states"]
     policy = cfg["simulator"]["policy"]
     logflag = cfg["simulator"]["logging"]
     # logging not yet implemented
@@ -238,6 +257,12 @@ def main(cfg: DictConfig):
     model.load_model(filename=save_path, scale_data=scale_data)
     # model.build_model(**cfg["model"]["build_params"])
 
+    if not initial_states:
+        logger.warn(
+            "No initial values provided, using randomly initialized states which is probably NOT what you want"
+        )
+        initial_states = {k: random.random() for k in states}
+
     # Grab standardized way to interact with sim API
     sim = Simulator(
         model,
@@ -247,6 +272,7 @@ def main(cfg: DictConfig):
         input_cols,
         output_cols,
         episode_inits,
+        initial_states,
         diff_state,
     )