Add hyper parameters for SQIL.

tuning/hp_search_spaces.py

@@ -18,7 +18,9 @@
 
 import optuna
 import sacred
+import stable_baselines3 as sb3
 
+import imitation.scripts.train_imitation
 import imitation.scripts.train_preference_comparisons
 
 
@@ -33,12 +35,16 @@ class RunSacredAsTrial:
     """The sacred experiment to run."""
     sacred_ex: sacred.Experiment
 
+
     """A function that returns a list of named configs to pass to sacred.run."""
     suggest_named_configs: Callable[[optuna.Trial], List[str]]
 
     """A function that returns a dict of config updates to pass to sacred.run."""
     suggest_config_updates: Callable[[optuna.Trial], Mapping[str, Any]]
 
+    """Command name to pass to sacred.run."""
+    command_name: str = None
+
     def __call__(
             self,
             trial: optuna.Trial,
@@ -61,6 +67,7 @@ def __call__(
 
         experiment: sacred.Experiment = self.sacred_ex
         result = experiment.run(
+            command_name=self.command_name,
             config_updates=config_updates,
             named_configs=named_configs,
             options=run_options,
@@ -115,4 +122,41 @@ def __call__(
             },
         },
     ),
+    sqil=RunSacredAsTrial(
+        sacred_ex=imitation.scripts.train_imitation.train_imitation_ex,
+        command_name="sqil",
+        suggest_named_configs=lambda _: [],
+        suggest_config_updates=lambda trial: {
+            "seed": trial.number,
+            "demonstrations": {
+                "n_expert_demos": 100,
+                "source": "generated",
+            },
+            "sqil": {
+                "total_timesteps": 1e6,
+                "train_kwargs": {
+
+                }
+            },
+            "rl": {
+                "rl_cls": sb3.DQN,
+                "rl_kwargs": {
+                    "learning_rate": trial.suggest_float("learning_rate", 1e-6, 1e-2, log=True),
+                    "buffer_size": trial.suggest_int("buffer_size", 1000, 100000),
+                    "learning_starts": trial.suggest_int("learning_starts", 1000, 10000),
+                    "batch_size": trial.suggest_int("batch_size", 32, 128),
+                    "tau": trial.suggest_float("tau", 0., 1.),
+                    "gamma": trial.suggest_float("gamma", 0.9, 0.999),
+                    "train_freq": trial.suggest_int("train_freq", 1, 40),
+                    "gradient_steps": trial.suggest_int("gradient_steps", 1, 10),
+                    "target_update_interval": trial.suggest_int("target_update_interval", 1, 10000),
+                    "exploration_fraction": trial.suggest_float("exploration_fraction", 0.01, 0.5),
+                    "exploration_final_eps": trial.suggest_float("exploration_final_eps", 0.01, 1.0),
+                    "exploration_initial_eps": trial.suggest_float("exploration_initial_eps", 0.01, 0.5),
+                    "max_grad_norm": trial.suggest_float("max_grad_norm", 0.1, 10.0),
+
+                },
+            },
+        },
+    ),
 )