added file to start algo task; related to #16 ;

RepeatCopy-PPO.py

@@ -0,0 +1,92 @@
+from sklearn.model_selection import GridSearchCV
+import pandas as pd
+import namesgenerator
+from ppo import main
+import json
+
+
+default_hyperparameters = {
+    'VERSION': '2.1.0',
+    'ENV': 'RepeatCopy-v0',
+    'RANDOM_SEED': 42,
+    'RENDER': False,
+
+    'MLP_UNITS': 16,
+    'MLP_LAYERS': 1,  # one shared hidden layer between V and P
+    'P_MLP_LAYERS': 0,  # policy network is linear
+    'V_MLP_LAYERS': 1,  # V network is "deep"
+
+    # the dimensionality for all these is: "MDP state transitions" (not observational frames)
+    'GRADIENT_LEARNING_BATCH_SIZE': 32,
+    'TRANSITIONS_IN_EXPERIENCE_BUFFER': 1024,
+    'HORIZON': 1024,
+    'TOTAL_ENV_STEPS': 1e5,  # 2e7,
+
+    'EPOCHS_PER_UPDATE': 4,
+
+    'CLIP_RANGE': .2,
+    'GAMMA': .99,
+    'ADVANTAGE_LAMBDA': .97,
+    'VALUE_LAMBDA': .99,
+    'MAX_GRAD_NORM': .5,
+    'VALUE_LOSS_WEIGHT': .25,
+    'LR': 3e-4,
+}
+
+
+class RLEstimator():
+    params_default = default_hyperparameters
+    params_values = {}
+    score_ = None
+    _flushed_score = False
+    _random_name = ''
+
+    def __init__(self, *args, **kwargs):
+        self.set_params(**kwargs)
+
+    def get_params(self, *args, **kwargs):
+        return self.params_values
+
+    def set_params(self, *args, **kwargs):
+        for param, param_default_value in self.params_default.items():
+            if param in kwargs:
+                self.params_values[param] = kwargs[param]
+            elif param in self.params_values:
+                pass
+            else:
+                self.params_values[param] = param_default_value
+
+    def fit(self, X):
+        self._random_name = namesgenerator.get_random_name()
+        self.score_ = main(hparams=self.params_values,
+                           random_name=self._random_name)
+        return self
+
+    def score(self, X):
+        if not self._flushed_score:
+            self._flushed_score = True
+            with open('score.log', 'a') as log:
+                log.write(json.dumps(
+                    {'score': self.score_, 'random_name': self._random_name, **self.params_values}) + "\n")
+        return self.score_
+
+
+if __name__ == '__main__':
+    # main(hparams=default_hyperparameters)
+    # exit()
+    INITS_PER_HYPERSET = 2
+    assert INITS_PER_HYPERSET % 2 == 0
+    rle = RLEstimator()
+    gs = GridSearchCV(rle, {
+        'RENDER': [False],
+        'RANDOM_SEEED': [False],
+        'GAMMA': [0.95, 0.99, 0.9],
+        'ADVANTAGE_LAMBDA': [0.8, 0.9, 0.95],
+        'VALUE_LAMBDA': [0.8, 0.9, 0.95],
+        'TRANSITIONS_IN_EXPERIENCE_BUFFER': [128, 256, 512, 1024],
+        'GRADIENT_LEARNING_BATCH_SIZE': [16, 32, 64, 128],
+        'TOTAL_ENV_STEPS': [10_000],
+    }, cv=INITS_PER_HYPERSET, n_jobs=-1, refit=False)
+    gs.fit([.0]*INITS_PER_HYPERSET)
+    print(pd.DataFrame(gs.cv_results_).filter(
+        regex='^(param_)|(mean_test_score)'))