[WIP] TurBO Benchmarks: robot pushing

hpobench/benchmarks/rl/robot_pushing_benchmark.py

@@ -0,0 +1,143 @@
+"""
+How to use this benchmark:
+--------------------------
+
+We recommend using the containerized version of this benchmark.
+If you want to use this benchmark locally (without running it via the corresponding container),
+you need to perform the following steps.
+
+Prerequisites:
+==============
+Conda environment in which the HPOBench is installed (pip install .). Activate your environment.
+```
+conda activate <Name_of_Conda_HPOBench_environment>
+pip install pygame==2.0.1 box2d-py==2.3.8
+```
+
+1. Clone from github:
+=====================
+```
+git clone HPOBench
+```
+
+2. Clone and install
+====================
+```
+cd /path/to/HPOBench
+pip install .[paramnet]
+
+```
+
+Changelog:
+==========
+
+0.0.1:
+* First implementation
+"""
+import logging
+from typing import Union, Dict
+
+import ConfigSpace as CS
+import numpy as np
+
+from hpobench.abstract_benchmark import AbstractBenchmark
+from hpobench.util.benchmarks.rl.robot_pushing_utils import PushReward
+
+__version__ = '0.0.1'
+
+logger = logging.getLogger('RobotPushing')
+
+
+class RobotPushingBenchmark(AbstractBenchmark):
+
+    def __init__(self,
+                 rng: Union[np.random.RandomState, int, None] = None):
+        """
+        Parameters
+        ----------
+        rng : np.random.RandomState, int, None
+        """
+        self.simulation = PushReward(gui=False)
+
+        super(RobotPushingBenchmark, self).__init__(rng=rng)
+
+    @staticmethod
+    def get_configuration_space(seed: Union[int, None] = None) -> CS.ConfigurationSpace:
+        from hpobench.util.benchmarks.rl.robot_pushing_utils import PushReward
+        xmin, xmax = PushReward.get_limits()
+
+        cs = CS.ConfigurationSpace(seed=seed)
+        cs.add_hyperparameters([
+            CS.UniformFloatHyperparameter('rx', lower=xmin[0], upper=xmax[0]),
+            CS.UniformFloatHyperparameter('ry', lower=xmin[1], upper=xmax[1]),
+            CS.UniformFloatHyperparameter('xvel', lower=xmin[2], upper=xmax[2]),
+            CS.UniformFloatHyperparameter('yvel', lower=xmin[3], upper=xmax[3]),
+            CS.UniformFloatHyperparameter('simu_steps', lower=xmin[4], upper=xmax[4]),
+            CS.UniformFloatHyperparameter('init_angle', lower=xmin[5], upper=xmax[5]),
+            CS.UniformFloatHyperparameter('rx2', lower=xmin[6], upper=xmax[6]),
+            CS.UniformFloatHyperparameter('ry2', lower=xmin[7], upper=xmax[7]),
+            CS.UniformFloatHyperparameter('xvel2', lower=xmin[8], upper=xmax[8]),
+            CS.UniformFloatHyperparameter('yvel2', lower=xmin[9], upper=xmax[9]),
+            CS.UniformFloatHyperparameter('simu_steps2', lower=xmin[10], upper=xmax[10]),
+            CS.UniformFloatHyperparameter('init_angle2', lower=xmin[11], upper=xmax[11]),
+            CS.UniformFloatHyperparameter('rtor', lower=xmin[12], upper=xmax[12]),
+            CS.UniformFloatHyperparameter('rtor2', lower=xmin[13], upper=xmax[13]),
+            ])
+        return cs
+
+    @staticmethod
+    def get_fidelity_space(seed: Union[int, None] = None) -> CS.ConfigurationSpace:
+        logger.debug('This benchmark has no fidelities.')
+        return CS.ConfigurationSpace()
+
+    @staticmethod
+    def get_meta_information():
+        """ Returns the meta information for the benchmark """
+        return {'name': 'Robot Pushing Benchmark',
+                'references': ['@inproceedings{wang2018batched,'
+                               'title        = {Batched large-scale bayesian optimization in high-dimensional spaces},'
+                               'author       = {Wang, Zi and Gehring, Clement and Kohli, Pushmeet '
+                               '                and Jegelka, Stefanie},'
+                               'booktitle    = {International Conference on Artificial Intelligence and Statistics},'
+                               'pages        = {745--754},'
+                               'year         = {2018},'
+                               'organization = {PMLR}'
+                               '}',
+
+                               '@inproceedings{eriksson2019scalable,'
+                               'title     = {Scalable Global Optimization via Local {Bayesian} Optimization},'
+                               'author    = {Eriksson, David and Pearce, Michael and Gardner, Jacob and Turner, Ryan D '
+                               '             and Poloczek, Matthias},'
+                               'booktitle = {Advances in Neural Information Processing Systems},'
+                               'pages     = {5496--5507},'
+                               'year      = {2019},'
+                               'url = {http://papers.nips.cc/paper/8788-scalable-global-optimization-via-local-bayesian'
+                               '-optimization.pdf}'
+                               '}'],
+                'code': 'https://github.com/zi-w/Ensemble-Bayesian-Optimization/tree/master/test_functions'
+                        'https://github.com/uber-research/TuRBO'
+                }
+
+    @AbstractBenchmark.check_parameters
+    def objective_function(self, configuration: Union[CS.Configuration, Dict],
+                           fidelity: Union[CS.Configuration, Dict, None] = None,
+                           rng: Union[np.random.RandomState, int, None] = None, **kwargs) -> Dict:
+
+        from time import time
+
+        start_time = time()
+
+        distance_to_goal = self.simulation(configuration)
+
+        finish_time = time()
+
+        return {'function_value': distance_to_goal,
+                "cost": finish_time - start_time,
+                'info': {'fidelity': None}}
+
+    @AbstractBenchmark.check_parameters
+    def objective_function_test(self, configuration: Union[CS.Configuration, Dict],
+                                fidelity: Union[CS.Configuration, Dict, None] = None,
+                                rng: Union[np.random.RandomState, int, None] = None, **kwargs) -> Dict:
+
+        return self.objective_function(configuration, fidelity, rng, **kwargs)