From cf687846d858d47716aafa1615bbf7368eaf4ca5 Mon Sep 17 00:00:00 2001 From: Sam Wenke Date: Mon, 6 May 2019 14:48:02 -0400 Subject: [PATCH] V1 Proposal (#1) * initial v1 proposal * Fix formating, styling and linting issues as part of the refactor for v1. * Fix bug with np_random property being created after make_game is called. --- README.md | 33 ++-- aperture.gif | Bin 29943 -> 0 bytes gym_pycolab/__init__.py | 36 ++-- gym_pycolab/pycolab_env.py | 292 +++++++++++++------------------- gym_pycolab/pycolab_env_test.py | 103 +++++------ gym_pycolab/pycolab_games.py | 148 +++++++++------- setup.py | 2 +- 7 files changed, 287 insertions(+), 327 deletions(-) delete mode 100644 aperture.gif diff --git a/README.md b/README.md index 8b8311d..ccf59bf 100644 --- a/README.md +++ b/README.md @@ -7,9 +7,8 @@

Extraterrestrial Marauders - Aperture

-

Extraterrestrial Marauders and Aperture (env.render())

+

Extraterrestrial Marauders

```sh @@ -25,22 +24,28 @@ Inherit from `gym_pycolab.PyColabEnv` to make a `gym.Env` version. Also see [pyc ```python import gym_pycolab - -def make_game(): - my_game = ... - return my_game +from gym import spaces class MyGameEnv(gym_pycolab.PyColabEnv): """A pycolab game env.""" def __init__(self, - max_steps=10, + max_iterations=10, default_reward=-1.): super(MyGameEnv, self).__init__( - game_factory=make_game, - max_iterations=max_steps, + max_iterations=max_iterations, default_reward=default_reward, - num_actions=4) + action_space=spaces.Discrete(4)) + + def make_game(self): + return my_game + + def make_colors(self): + return {'#': (0, 0, 255)} + +env = MyGameEnv() +state = env.reset() +state, reward, done, info = env.step(0) ``` # Games and Envs @@ -51,7 +56,7 @@ Includes most of the pycolab example games (with `gym.make(game)` or `gym_pycola + `WarehouseManager-v{0,1,2}` ([warehouse_manager](https://github.com/deepmind/pycolab/blob/master/pycolab/examples/warehouse_manager.py)) + `FluvialNatation-v0` ([fluvial_natation](https://github.com/deepmind/pycolab/blob/master/pycolab/examples/fluvial_natation.py)) + `ExtraterrestrialMarauders-v0` ([extraterrestrial_marauders](https://github.com/deepmind/pycolab/blob/master/pycolab/examples/extraterrestrial_marauders.py)) -+ `Shockwave-v{0,1}` ([shockwave](https://github.com/deepmind/pycolab/blob/master/pycolab/examples/shockwave.py)) ++ `ShockWave-v{0,1}` ([shockwave](https://github.com/deepmind/pycolab/blob/master/pycolab/examples/shockwave.py)) + `Aperture-v{0,1,2}` ([aperture](https://github.com/deepmind/pycolab/blob/master/pycolab/examples/aperture.py)) + `Apprehend-v0` ([apprehend](https://github.com/deepmind/pycolab/blob/master/pycolab/examples/apprehend.py)) + `BetterScrollyMaze-v{0,1,2}` ([better_scrolly_maze](https://github.com/deepmind/pycolab/blob/master/pycolab/examples/better_scrolly_maze.py)) @@ -74,4 +79,10 @@ Development is started with `pipenv`. ```sh $ pipenv install $ pipenv shell +``` + +# Testing + +```sh +$ python -m gym_pycolab.pycolab_env_test ``` \ No newline at end of file diff --git a/aperture.gif b/aperture.gif deleted file mode 100644 index 7c4b9e9af91c7136019129a77791e7cde686425a..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 29943 zcmZ^LWmH@3wk}?R6eq#m-6>EC6nEF+?(XjH?(XjH?(XjHQYdY?^xONK-E;1}VT2zf zBje3l(;k_KhzWCY=K0K);J{qwFr#{h$V%i)VKFwHV4tFk!nvZ0-J znEcqMbM0~c*0Uoc!U!8ZQeaog3QCFn$0tl{S!Ozj^Qz#Sez8WrnrB@IYQPD$~!K+ep{&Ov61b51QP zh)cjIuc)lTNI$x$ znZEgf#ifRo^}~+M?SXVtn7w7jquchA)1Gsa%d7pHyI&~}k5`$a#WGl5ynllgp_v5f z@Y})eQ~eyOiTLV?Op=t&SxKB4ex*k^Lm5rNXkD}R=7UZVS0Kni*`13Jj4~vejH8>C z;2LKnn$Bt86#B`sXw0u(9cJ;VXlqKxT0rcvz@fCBL$DA_DHLuxJY6}xGnLOtKuVDL zV?gH_48)v<$$S;Y`jh3iR{QKlAvcbowT|N_-iy~g1|4m`X;dTK=bF+ICt$P5#k2Na zcAScAl?8x#dxECNuIs>+@dQ0j#J+tZ?#5@iM!#SBb6dmSJh_Q|QzVu%0J4qd-fB{~ z>2`le3Cfvg13RT^CZAi)tHrgBL7`BXa=erKyMcm|aa|NbJB6oocxNQxmK{!WcDsi_ zwin370DjHc=yGG+^~a;8O*Y-jsmK=houj<$n@ZY^J+YQ(4{J5bJzCN-BR%=wg&^XfXUpe2eo8;kjvJ2MNNDgkR>J)#>qi%o zZ0RMf<}Hf 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Only one object - class is represented, following the rendering order of the - game. For example, Bridge = 1, Water = 2 -> [[1, ...]], - the Bridge is represented instead of the Water. - - rgb: TODO(wenkesj): docstring. - exclude_from_state: set to exclude from the observations to states. - merge_layer_groups: merge layers for these group of observations keys. """ - assert observation_type in ['layers', 'labels', 'rgb'] assert max_iterations > 0 assert isinstance(default_reward, numbers.Number) - self._game_factory = game_factory self._max_iterations = max_iterations self._default_reward = default_reward - if callable(colors): - self._colors = None - self._colors_factory = colors - else: - self._colors = colors - self._colors_factory = None + + # At this point, the game would only want to access the random + # property, although it is set to None initially. self.np_random = None - test_game = self._game_factory() - setattr(test_game.the_plot, 'info', {}) + self._colors = self.make_colors() + test_game = self.make_game() + test_game.the_plot.info = {} observations, _, _ = test_game.its_showtime() layers = list(observations.layers.keys()) not_ordered = list(set(layers) - set(test_game.z_order)) - self._render_order = list(reversed(not_ordered + test_game.z_order)) - if exclude_from_state is None: - exclude_from_state = [] - self._exclude_from_state = set(exclude_from_state) - - if merge_layer_groups is None: - merge_layer_groups = [set([])] - self._merge_layer_groups = merge_layer_groups - - observation_layers = list(set(layers) - self._exclude_from_state) + # Create the observation space. + observation_layers = list(set(layers)) self._observation_order = sorted(observation_layers) - self._observation_type = observation_type - - if self._observation_type == 'layers': - merge_size_reduction = 0 - for group in self._merge_layer_groups: - if group: - merge_size_reduction += len(group) - 1 - channels = [len(observation_layers) - merge_size_reduction] - channel_max = 1. - channel_min = 0. - self.get_states = self._get_states_layers - elif self._observation_type == 'labels': - # TODO(wenkesj): implement merge_layer_groups - channels = [] - channel_max = 1. - channel_min = 0. - self.get_states = self._get_states_labels - elif self._observation_type == 'rgb': - channels = [3] - channel_max = 255. - channel_min = 0. - self.get_states = self._get_states_rgb - + channels = [3] + channel_max = 255. + channel_min = 0. self._game_shape = list(observations.board.shape) + channels self.observation_space = spaces.Box( - low=np.ones(self._game_shape, np.float32) * channel_min, - high=np.ones(self._game_shape, np.float32) * channel_max, + low=np.full(self._game_shape, channel_min, np.float32), + high=np.full(self._game_shape, channel_max, np.float32), dtype=np.float32) self.action_space = action_space self.current_game = None - self._last_observations = None self._empty_board = None self._last_state = None self._last_reward = None - self._game_over = False + self._game_over = False self.viewer = None self.resize_scale = resize_scale self.delay = delay - def _get_states_layers(self, observations): - """Transform the pycolab `rendering.Observations` to a state by layers.""" - layered_observations = np.stack([ - np.asarray(observations.layers[layer_key], np.float32) - for layer_key in self._observation_order], axis=-1) - - # TODO(wenkesj): is there a faster/better way to do this? - # We can probably precompute this and change the observation_order. - if self._merge_layer_groups[0]: - for group_set in self._merge_layer_groups: - group = list(group_set) - group_layers = [] - group_remove_indices = [] - - leader_key = group[0] - leader_layer_idx = self._observation_order.index(leader_key) - leader_layer = layered_observations[..., leader_layer_idx] - group_layers.append(leader_layer) - - for key in group[1:]: - layer_idx = self._observation_order.index(key) - group_remove_indices.append(layer_idx) - layer = layered_observations[..., layer_idx] - group_layers.append(layer) - - # remove layers that are merged. - layered_observations[..., leader_layer_idx] = np.logical_or.reduce(group_layers) - layered_observations = np.delete(layered_observations, group_remove_indices, -1) - return layered_observations - - # TODO(wenkesj): implement merge_layer_groups for labels. - def _get_states_labels(self, observations): - """Transform the pycolab `rendering.Observations` to a state by label.""" - board = np.zeros(self._game_shape, np.int32) - board_mask = np.zeros(self._game_shape, np.int32) + @abc.abstractmethod + def make_game(self): + """Function that creates a new pycolab game. - for key in self._render_order: - if (key in self._exclude_from_state): - continue - board_layer_mask = np.array(observations.layers[key]) * self._observation_order.index(key) - board = np.where(np.logical_not(board_mask), board_layer_mask, board) - board_mask = np.logical_or(board_layer_mask, board_mask) - return board.astype(np.float32) + Returns: + pycolab.Engine. + """ + pass - def _get_states_rgb(self, observations): - """Transform the pycolab `rendering.Observations` to a state by rgb.""" - board = self._paint_board( - observations.layers, exclude=True).astype(np.float32) - return board + def make_colors(self): + """Functions that returns colors. + + Returns: + Dictionary mapping key name to `tuple(R, G, B)`. + """ + return {} + + def _paint_board(self, layers): + """Method to privately paint layers to RGB. - def _paint_board(self, layers, exclude=False): - """Method to privately paint layers to RGB.""" + Args: + layers: a dictionary mapping a character to the respective curtain. + + Returns: + 3D np.array (np.uint32) representing the RGB of the observation + layers. + """ board_shape = self._last_observations.board.shape board = np.zeros(list(board_shape) + [3], np.uint32) board_mask = np.zeros(list(board_shape) + [3], np.bool) for key in self._render_order: - if exclude and (key in self._exclude_from_state): - continue - color = self._colors.get(key, (0, 0, 0)) color = np.reshape(color, [1, 1, -1]).astype(np.uint32) @@ -208,41 +140,23 @@ def _paint_board(self, layers, exclude=False): board_layer_mask = np.repeat(board_layer_mask, 3, axis=-1) # Update the board with the new layer. - board = np.where(np.logical_not(board_mask), board_layer_mask * color, board) + board = np.where( + np.logical_not(board_mask), + board_layer_mask * color, + board) # Update the mask. board_mask = np.logical_or(board_layer_mask, board_mask) return board - def paint_image(self, layers, board=None, resize=True, exclude=False): - """Paint the layers into the board and return an RGB array.""" - if self._colors: - img = self._paint_board(layers, exclude=exclude) - else: - assert board is not None, '`board` must not be `None` if there are no colors.' - img = board - if resize: - img = np.repeat(np.repeat( - img, self.resize_scale, axis=0), self.resize_scale, axis=1) - if len(img.shape) != 3: - img = np.repeat(img[..., None], 3, axis=-1) - img = img.astype(np.uint8) - return img - - def layers_from_board(self, board): - """Convert the observation board into into layers.""" - board_layers = np.rollaxis(board, axis=-1) - layers = {} - for idx, key in enumerate(self._observation_order): - layers[key] = board_layers[idx] - return layers - def _update_for_game_step(self, observations, reward): """Update internal state with data from an environment interaction.""" self._last_observations = observations self._empty_board = np.zeros_like(self._last_observations.board) - self._last_state = self.get_states(observations) - self._last_reward = reward if reward is not None else self._default_reward + self._last_state = self._paint_board(observations.layers).astype( + np.float32) + self._last_reward = reward if reward is not None else \ + self._default_reward self._game_over = self.current_game.game_over if self.current_game.the_plot.frame >= self._max_iterations: @@ -250,10 +164,9 @@ def _update_for_game_step(self, observations, reward): def reset(self): """Start a new episode.""" - self.current_game = self._game_factory() - if self._colors_factory: - self._colors = self._colors_factory() - setattr(self.current_game.the_plot, 'info', {}) + self.current_game = self.make_game() + self._colors = self.make_colors() + self.current_game.the_plot.info = {} self._game_over = None self._last_observations = None self._last_reward = None @@ -262,7 +175,14 @@ def reset(self): return self._last_state def step(self, action): - """Apply action, step the world forward, and return observations.""" + """Apply action, step the world forward, and return observations. + + Args: + action: the desired action to apply to the environment. + + Returns: + state, reward, done, info. + """ if self.current_game is None: logger.warn("Episode has already ended, call `reset` instead..") state = self._last_state @@ -271,10 +191,10 @@ def step(self, action): return state, reward, done, {} # Execute the action in pycolab. - setattr(self.current_game.the_plot, 'info', {}) + self.current_game.the_plot.info = {} observations, reward, _ = self.current_game.play(action) self._update_for_game_step(observations, reward) - info = getattr(self.current_game.the_plot, 'info', {}) + info = self.current_game.the_plot.info # Check the current status of the game. state = self._last_state @@ -286,37 +206,55 @@ def step(self, action): return state, reward, done, info def render(self, mode='human'): - """Render the board to the gym viewer.""" + """Render the board to an image viewer or an np.array. + + Args: + mode: One of the following modes: + - 'human': render to an image viewer. + - 'rgb_array': render to an RGB np.array (np.uint8) + + Returns: + 3D np.array (np.uint8) or a `viewer.isopen`. + """ + img = self._empty_board if self._last_observations: img = self._last_observations.board layers = self._last_observations.layers - img = self.paint_image(layers, board=img) - else: - img = self._empty_board - img = np.repeat(np.repeat( - img, self.resize_scale, axis=0), self.resize_scale, axis=1) - if len(img.shape) != 3: - img = np.repeat(img[..., None], 3, axis=-1) - img = img.astype(np.uint8) + if self._colors: + img = self._paint_board(layers) + else: + assert img is not None, '`board` must not be `None`.' + + img = _repeat_axes(img, self.resize_scale, axis=[0, 1]) + if len(img.shape) != 3: + img = np.repeat(img[..., None], 3, axis=-1) + img = img.astype(np.uint8) if mode == 'rgb_array': return img - elif mode == 'human': if self.viewer is None: - from gym.envs.classic_control.rendering import SimpleImageViewer + from gym.envs.classic_control.rendering import ( + SimpleImageViewer) self.viewer = SimpleImageViewer() self.viewer.imshow(img) time.sleep(self.delay / 1e3) return self.viewer.isopen def seed(self, seed=None): - """Seeds the environment.""" + """Seeds the environment. + + Args: + seed: seed of the random engine. + + Returns: + [seed]. + """ self.np_random, seed = seeding.np_random(seed) return [seed] def close(self): - """Sets up the renderer.""" + """Tears down the renderer.""" if self.viewer: self.viewer.close() self.viewer = None diff --git a/gym_pycolab/pycolab_env_test.py b/gym_pycolab/pycolab_env_test.py index 3309dc5..6534481 100644 --- a/gym_pycolab/pycolab_env_test.py +++ b/gym_pycolab/pycolab_env_test.py @@ -16,80 +16,69 @@ from gym_pycolab import pycolab_env +class CustomColorsFourRoomsEnv(pycolab_env.PyColabEnv): + """Classic four rooms game, with custom colors. + + Reference: + https://github.com/deepmind/pycolab/blob/master/pycolab/examples/classics/four_rooms.py + """ + + def __init__(self, + max_iterations=10, + default_reward=-1.): + super(CustomColorsFourRoomsEnv, self).__init__( + max_iterations=max_iterations, + default_reward=default_reward, + action_space=spaces.Discrete(4 + 1), + resize_scale=8) + + def make_game(self): + return four_rooms.make_game() + + def make_colors(self): + return { + 'P': (0, 0, 255), + ' ': (255, 0, 0), + '#': (0, 255, 0), + } + + class PyColabEnvTest(parameterized.TestCase): def setUp(self): super(PyColabEnvTest, self).setUp() - self._game_factory = four_rooms.make_game - self._action_space = spaces.Discrete(4 + 1) self._max_iterations = 10 self._default_reward = 0 - self._resize_scale = 8 - self._colors = { - 'P': (0, 0, 255), - ' ': (255, 0, 0), - '#': (0, 255, 0), - } - self._observation_type = 'layers' - - def testBadObservationTypeConstructor(self): - with self.assertRaises(AssertionError): - _ = pycolab_env.PyColabEnv( - game_factory=self._game_factory, - action_space=self._action_space, - max_iterations=self._max_iterations, - default_reward=self._default_reward, - resize_scale=self._resize_scale, - observation_type='bad_observation_type') def testBadMaxIterationsConstructor(self): with self.assertRaises(AssertionError): - _ = pycolab_env.PyColabEnv( - game_factory=self._game_factory, - action_space=self._action_space, + _ = CustomColorsFourRoomsEnv( max_iterations=-1, - default_reward=self._default_reward, - resize_scale=self._resize_scale, - observation_type=self._observation_type) + default_reward=self._default_reward) def testBadDefaultRewardConstructor(self): with self.assertRaises(AssertionError): - _ = pycolab_env.PyColabEnv( - game_factory=self._game_factory, - action_space=self._action_space, + _ = CustomColorsFourRoomsEnv( max_iterations=self._max_iterations, - default_reward=None, - resize_scale=self._resize_scale, - observation_type=self._observation_type) - - @parameterized.named_parameters( - ('Layers', 'layers', (13, 13, 3)), - ('Labels', 'labels', (13, 13)), - ('RGB', 'rgb', (13, 13, 3))) - def testObservationType(self, observation_type, shape): - env = pycolab_env.PyColabEnv( - game_factory=self._game_factory, - action_space=self._action_space, + default_reward=None) + + @parameterized.parameters( + ((13, 13, 3),),) + def testReset(self, shape): + env = CustomColorsFourRoomsEnv( max_iterations=self._max_iterations, - default_reward=self._default_reward, - colors=self._colors, - resize_scale=self._resize_scale, - observation_type=observation_type) + default_reward=self._default_reward) last_state = env.reset() self.assertEqual(shape, last_state.shape) @parameterized.parameters( - (1, (0, 1, 2)), - (2, (0, 1)), + (1, (0, 1, 2)), + (2, (0, 1)), (10, (0, 1, 2, 3, 0, 0, 0, 0, 0, 0, 0))) def testMaxIterations(self, max_iterations, actions): - env = pycolab_env.PyColabEnv( - game_factory=self._game_factory, - action_space=self._action_space, + env = CustomColorsFourRoomsEnv( max_iterations=max_iterations, - default_reward=self._default_reward, - resize_scale=self._resize_scale, - observation_type=self._observation_type) + default_reward=self._default_reward) _ = env.reset() for step, action in enumerate(actions): @@ -102,13 +91,9 @@ def testMaxIterations(self, max_iterations, actions): (-1., -3, (0, 1, 2)), (0., 1., (0, 0, 0, 0, 3, 3, 0, 0, 0, 2))) def testTotalRewards(self, default_reward, expected_total_reward, actions): - env = pycolab_env.PyColabEnv( - game_factory=self._game_factory, - action_space=self._action_space, + env = CustomColorsFourRoomsEnv( max_iterations=self._max_iterations, - default_reward=default_reward, - resize_scale=self._resize_scale, - observation_type=self._observation_type) + default_reward=default_reward) _ = env.reset() total_reward = 0. @@ -120,4 +105,4 @@ def testTotalRewards(self, default_reward, expected_total_reward, actions): if __name__ == '__main__': - absltest.main() \ No newline at end of file + absltest.main() diff --git a/gym_pycolab/pycolab_games.py b/gym_pycolab/pycolab_games.py index 678dc44..a5be6af 100644 --- a/gym_pycolab/pycolab_games.py +++ b/gym_pycolab/pycolab_games.py @@ -30,16 +30,18 @@ class OrdealEnv(pycolab_env.PyColabEnv): https://github.com/deepmind/pycolab/blob/master/pycolab/examples/ordeal.py """ - def __init__(self, - max_steps=10, + def __init__(self, + max_iterations=10, default_reward=-1.): super(OrdealEnv, self).__init__( - game_factory=ordeal.make_game, - max_iterations=max_steps, + max_iterations=max_iterations, default_reward=default_reward, action_space=spaces.Discrete(3 + 1), resize_scale=8) + def make_game(self): + return ordeal.make_game() + class WarehouseManagerEnv(pycolab_env.PyColabEnv): """Warehouse manager game. @@ -48,17 +50,20 @@ class WarehouseManagerEnv(pycolab_env.PyColabEnv): https://github.com/deepmind/pycolab/blob/master/pycolab/examples/warehouse_manager.py """ - def __init__(self, + def __init__(self, level=0, - max_steps=10, + max_iterations=10, default_reward=-1.): + self.level = level super(WarehouseManagerEnv, self).__init__( - game_factory=lambda: warehouse_manager.make_game(level), - max_iterations=max_steps, + max_iterations=max_iterations, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), resize_scale=8) + def make_game(self): + return warehouse_manager.make_game(self.level) + class FluvialNatationEnv(pycolab_env.PyColabEnv): """Fluvial natation game. @@ -67,16 +72,18 @@ class FluvialNatationEnv(pycolab_env.PyColabEnv): https://github.com/deepmind/pycolab/blob/master/pycolab/examples/fluvial_natation.py """ - def __init__(self, - max_steps=10, + def __init__(self, + max_iterations=10, default_reward=-1.): super(FluvialNatationEnv, self).__init__( - game_factory=fluvial_natation.make_game, - max_iterations=max_steps, + max_iterations=max_iterations, default_reward=default_reward, action_space=spaces.Discrete(2 + 1), resize_scale=8) + def make_game(self): + return fluvial_natation.make_game() + class ChainWalkEnv(pycolab_env.PyColabEnv): """Classic chain walk game. @@ -85,16 +92,18 @@ class ChainWalkEnv(pycolab_env.PyColabEnv): https://github.com/deepmind/pycolab/blob/master/pycolab/examples/classics/chain_walk.py """ - def __init__(self, - max_steps=10, + def __init__(self, + max_iterations=10, default_reward=-1.): super(ChainWalkEnv, self).__init__( - game_factory=chain_walk.make_game, - max_iterations=max_steps, + max_iterations=max_iterations, default_reward=default_reward, action_space=spaces.Discrete(2 + 1), resize_scale=8) + def make_game(self): + return chain_walk.make_game() + class CliffWalkEnv(pycolab_env.PyColabEnv): """Classic cliff walk game. @@ -103,16 +112,18 @@ class CliffWalkEnv(pycolab_env.PyColabEnv): https://github.com/deepmind/pycolab/blob/master/pycolab/examples/classics/cliff_walk.py """ - def __init__(self, - max_steps=10, + def __init__(self, + max_iterations=10, default_reward=-1.): super(CliffWalkEnv, self).__init__( - game_factory=cliff_walk.make_game, - max_iterations=max_steps, + max_iterations=max_iterations, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), resize_scale=8) + def make_game(self): + return cliff_walk.make_game() + class FourRoomsEnv(pycolab_env.PyColabEnv): """Classic four rooms game. @@ -121,16 +132,18 @@ class FourRoomsEnv(pycolab_env.PyColabEnv): https://github.com/deepmind/pycolab/blob/master/pycolab/examples/classics/four_rooms.py """ - def __init__(self, - max_steps=10, + def __init__(self, + max_iterations=10, default_reward=-1.): super(FourRoomsEnv, self).__init__( - game_factory=four_rooms.make_game, - max_iterations=max_steps, + max_iterations=max_iterations, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), resize_scale=8) + def make_game(self): + return four_rooms.make_game() + class ExtraterrestrialMaraudersEnv(pycolab_env.PyColabEnv): """Extraterrestrial marauders game. @@ -139,16 +152,18 @@ class ExtraterrestrialMaraudersEnv(pycolab_env.PyColabEnv): https://github.com/deepmind/pycolab/blob/master/pycolab/examples/extraterrestrial_marauders.py """ - def __init__(self, - max_steps=10, + def __init__(self, + max_iterations=10, default_reward=-1.): super(ExtraterrestrialMaraudersEnv, self).__init__( - game_factory=extraterrestrial_marauders.make_game, - max_iterations=max_steps, + max_iterations=max_iterations, default_reward=default_reward, action_space=spaces.Discrete(3 + 1), resize_scale=8) + def make_game(self): + return extraterrestrial_marauders.make_game() + class ShockWaveEnv(pycolab_env.PyColabEnv): """Shock wave game. @@ -157,17 +172,20 @@ class ShockWaveEnv(pycolab_env.PyColabEnv): https://github.com/deepmind/pycolab/blob/master/pycolab/examples/shockwave.py """ - def __init__(self, + def __init__(self, level=0, - max_steps=10, + max_iterations=10, default_reward=-1.): + self.level = level super(ShockWaveEnv, self).__init__( - game_factory=lambda: shockwave.make_game(level), - max_iterations=max_steps, + max_iterations=max_iterations, default_reward=default_reward, action_space=spaces.Discrete(3 + 1), resize_scale=8) + def make_game(self): + return shockwave.make_game(self.level) + class ApertureEnv(pycolab_env.PyColabEnv): """Aperature game. @@ -176,17 +194,20 @@ class ApertureEnv(pycolab_env.PyColabEnv): https://github.com/deepmind/pycolab/blob/master/pycolab/examples/aperture.py """ - def __init__(self, + def __init__(self, level=0, - max_steps=10, + max_iterations=10, default_reward=-1.): + self.level = level super(ApertureEnv, self).__init__( - game_factory=lambda: aperture.make_game(level), - max_iterations=max_steps, + max_iterations=max_iterations, default_reward=default_reward, action_space=spaces.Discrete(8 + 1), resize_scale=16) + def make_game(self): + return aperture.make_game(self.level) + class ApprehendEnv(pycolab_env.PyColabEnv): """Apprehend game. @@ -195,16 +216,18 @@ class ApprehendEnv(pycolab_env.PyColabEnv): https://github.com/deepmind/pycolab/blob/master/pycolab/examples/apprehend.py """ - def __init__(self, - max_steps=10, + def __init__(self, + max_iterations=10, default_reward=-1.): super(ApprehendEnv, self).__init__( - game_factory=apprehend.make_game, - max_iterations=max_steps, + max_iterations=max_iterations, default_reward=default_reward, action_space=spaces.Discrete(2 + 1), resize_scale=8) + def make_game(self): + return apprehend.make_game() + class BetterScrollyMazeEnv(pycolab_env.PyColabEnv): """Better scrolly maze game. @@ -213,17 +236,20 @@ class BetterScrollyMazeEnv(pycolab_env.PyColabEnv): https://github.com/deepmind/pycolab/blob/master/pycolab/examples/better_scrolly_maze.py """ - def __init__(self, + def __init__(self, level=0, - max_steps=10, + max_iterations=10, default_reward=-1.): + self.level = level super(BetterScrollyMazeEnv, self).__init__( - game_factory=lambda: better_scrolly_maze.make_game(level), - max_iterations=max_steps, + max_iterations=max_iterations, default_reward=default_reward, action_space=spaces.Discrete(4 + 1), resize_scale=8) + def make_game(self): + return better_scrolly_maze.make_game(self.level) + if __name__ == "__main__": import argparse @@ -231,10 +257,10 @@ def __init__(self, parser = argparse.ArgumentParser() parser.add_argument( - '--game', + '--game', choices=[ - 'chain_walk', - 'cliff_walk', + 'chain_walk', + 'cliff_walk', 'four_rooms', 'extraterrestrial_marauders', 'shockwave', @@ -243,33 +269,33 @@ def __init__(self, 'better_scrolly_maze', 'ordeal', 'fluvial_natation', - 'warehouse_manager'], + 'warehouse_manager'], required=True) parser.add_argument('--benchmark', action='store_true') args = parser.parse_args() if args.game == 'chain_walk': - env = ChainWalkEnv(max_steps=250) + env = ChainWalkEnv(max_iterations=250) elif args.game == 'cliff_walk': - env = CliffWalkEnv(max_steps=250) + env = CliffWalkEnv(max_iterations=250) elif args.game == 'four_rooms': - env = FourRoomsEnv(max_steps=250) + env = FourRoomsEnv(max_iterations=250) elif args.game == 'extraterrestrial_marauders': - env = ExtraterrestrialMaraudersEnv(max_steps=250) + env = ExtraterrestrialMaraudersEnv(max_iterations=250) elif args.game == 'shockwave': - env = ShockWaveEnv(max_steps=250) + env = ShockWaveEnv(max_iterations=250) elif args.game == 'aperture': - env = ApertureEnv(max_steps=250) + env = ApertureEnv(max_iterations=250) elif args.game == 'apprehend': - env = ApprehendEnv(max_steps=250) + env = ApprehendEnv(max_iterations=250) elif args.game == 'better_scrolly_maze': - env = BetterScrollyMazeEnv(max_steps=250) + env = BetterScrollyMazeEnv(max_iterations=250) elif args.game == 'ordeal': - env = OrdealEnv(max_steps=250) + env = OrdealEnv(max_iterations=250) elif args.game == 'warehouse_manager': - env = WarehouseManagerEnv(max_steps=250) + env = WarehouseManagerEnv(max_iterations=250) elif args.game == 'fluvial_natation': - env = FluvialNatationEnv(max_steps=250) + env = FluvialNatationEnv(max_iterations=250) if args.benchmark: import time @@ -290,8 +316,8 @@ def __init__(self, average_eps_time = total_eps_time / num_eps average_fps = total_fps / num_eps print('total eps: {}ms, avg. eps: {}ms, avg. fps: {}fps'.format( - total_eps_time * 1e3, - average_eps_time * 1e3, + total_eps_time * 1e3, + average_eps_time * 1e3, average_fps)) else: state = env.reset() diff --git a/setup.py b/setup.py index 29aaf97..9c1c810 100644 --- a/setup.py +++ b/setup.py @@ -11,4 +11,4 @@ description='Gym interface for custom pycolab games.', url='https://github.com/fomorians/gym_pycolab', packages=find_packages(), - install_requires=['pycolab', 'gym']) \ No newline at end of file + install_requires=['pycolab', 'gym'])