Add hp search space for PC/classic control.

Author: ernestum

tuning/hp_search_spaces.py

@@ -123,6 +123,47 @@ def __call__(
             },
         },
     ),
+    pc_classic_control=RunSacredAsTrial(
+        sacred_ex=imitation.scripts.train_preference_comparisons.train_preference_comparisons_ex,
+        suggest_named_configs=lambda _: ["reward.reward_ensemble"],
+        suggest_config_updates=lambda trial: {
+            "seed": trial.number,
+            "environment": {"num_vec": 8},
+            "total_timesteps": 1e6,
+            "total_comparisons": 1000,
+            "active_selection": True,
+            "active_selection_oversampling": trial.suggest_int("active_selection_oversampling", 1, 11),
+            "comparison_queue_size": trial.suggest_int("comparison_queue_size", 1, 1001),  # upper bound determined by total_comparisons=1000
+            "exploration_frac": trial.suggest_float("exploration_frac", 0.0, 0.5),
+            "fragment_length": trial.suggest_int("fragment_length", 1, 1001),  # trajectories are 1000 steps long
+            "gatherer_kwargs": {
+                "temperature": trial.suggest_float("gatherer_temperature", 0.0, 2.0),
+                "discount_factor": trial.suggest_float("gatherer_discount_factor", 0.95, 1.0),
+                "sample": trial.suggest_categorical("gatherer_sample", [True, False]),
+            },
+            "initial_epoch_multiplier": trial.suggest_float("initial_epoch_multiplier", 1, 200.0),
+            "initial_comparison_frac": trial.suggest_float("initial_comparison_frac", 0.01, 1.0),
+            "num_iterations": trial.suggest_int("num_iterations", 1, 51),
+            "preference_model_kwargs": {
+                "noise_prob": trial.suggest_float("preference_model_noise_prob", 0.0, 0.1),
+                "discount_factor": trial.suggest_float("preference_model_discount_factor", 0.95, 1.0),
+            },
+            "query_schedule": trial.suggest_categorical("query_schedule", ["hyperbolic", "constant", "inverse_quadratic"]),
+            "trajectory_generator_kwargs": {
+                "switch_prob": trial.suggest_float("tr_gen_switch_prob", 0.1, 1),
+                "random_prob": trial.suggest_float("tr_gen_random_prob", 0.1, 0.9),
+            },
+            "transition_oversampling": trial.suggest_float("transition_oversampling", 0.9, 2.0),
+            "reward_trainer_kwargs": {
+                "epochs": trial.suggest_int("reward_trainer_epochs", 1, 11),
+            },
+            "rl": {
+                "rl_kwargs": {
+                    "ent_coef": trial.suggest_float("rl_ent_coef", 1e-7, 1e-3, log=True),
+                },
+            },
+        },
+    ),
     sqil=RunSacredAsTrial(
         sacred_ex=imitation.scripts.train_imitation.train_imitation_ex,
         command_name="sqil",