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size; OK provided power of two\n+                # PPO2 default is 2^2 = 4; run_humanoid.py is 2^5 = 32\n+                'nminibatches': tune.sample_from(\n+                    lambda spec: 2 ** (np.random.randint(0, 7))),\n+\n+                # PPO2 default is 4; run_humanoid.py is 10\n+                'noptepochs': tune.sample_from(\n+                    lambda spec: np.random.randint(1, 11)),\n+                },\n+    # PPO2 default is 3e-4; run_humanoid uses 1e-4;\n+    # Bansal et al use 1e-2 (but with huge batch size).\n+    # Sample log-uniform between 1e-2 and 1e-5.\n+    'learning_rate': tune.sample_from(\n+        lambda spec: 10 ** (-2 + -3 * np.random.random())),\n+}\n+\n+MULTI_TRAIN_LOCATION = 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this is sad -- it'll break on local machines.\r\n\r\nMaybe we should introduce a helper method, `get_data_location()`, that can default to just `data/` (relative location) but will look in an environment variable `ADV_DATA_ROOT` that we can then set in the Ray config to 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