install mpi4py in mpi dockerfile

.travis.yml

@@ -5,10 +5,14 @@ python:
 services:
     - docker
 
+env:
+  - DOCKER_SUFFIX=py36-nompi
+  - DOCKER_SUFFIX=py36-mpi
+
 install:
-    - pip install flake8
-    - docker build . -t baselines-test
+  - pip install flake8
+  - docker build -f test.dockerfile.${DOCKER_SUFFIX} -t baselines-test .
 
 script:
-    - flake8 . --show-source --statistics
-    - docker run baselines-test pytest -v --forked .
+  - flake8 . --show-source --statistics
+  - docker run baselines-test pytest -v .

README.md

@@ -109,9 +109,17 @@ python -m baselines.run --alg=ppo2 --env=PongNoFrameskip-v4 --num_timesteps=0 --
 
 *NOTE:* At the moment Mujoco training uses VecNormalize wrapper for the environment which is not being saved correctly; so loading the models trained on Mujoco will not work well if the environment is recreated. If necessary, you can work around that by replacing RunningMeanStd by TfRunningMeanStd in [baselines/common/vec_env/vec_normalize.py](baselines/common/vec_env/vec_normalize.py#L12). This way, mean and std of environment normalizing wrapper will be saved in tensorflow variables and included in the model file; however, training is slower that way - hence not including it by default
 
-## Loading and vizualizing learning curves and other training metrics
-See [here](docs/viz/viz.ipynb) for instructions on how to load and display the training data. 
 
+## Using baselines with TensorBoard
+Baselines logger can save data in the TensorBoard format. To do so, set environment variables `OPENAI_LOG_FORMAT` and `OPENAI_LOGDIR`:
+```bash
+export OPENAI_LOG_FORMAT='stdout,log,csv,tensorboard' # formats are comma-separated, but for tensorboard you only really need the last one
+export OPENAI_LOGDIR=path/to/tensorboard/data
+```
+And you can now start TensorBoard with:
+```bash
+tensorboard --logdir=$OPENAI_LOGDIR
+```
 ## Subpackages
 
 - [A2C](baselines/a2c)

baselines/common/cmd_util.py

@@ -60,14 +60,12 @@ def make_env(env_id, env_type, subrank=0, seed=None, reward_scale=1.0, gamestate
                   allow_early_resets=True)
 
     if env_type == 'atari':
-        env = wrap_deepmind(env, **wrapper_kwargs)
-    elif env_type == 'retro':
-        env = retro_wrappers.wrap_deepmind_retro(env, **wrapper_kwargs)
+         return wrap_deepmind(env, **wrapper_kwargs)
+    elif reward_scale != 1:
+         return retro_wrappers.RewardScaler(env, reward_scale)
+    else:
+        return env
 
-    if reward_scale != 1:
-        env = retro_wrappers.RewardScaler(env, reward_scale)
-
-    return env
 
 
 def make_mujoco_env(env_id, seed, reward_scale=1.0):
@@ -131,8 +129,6 @@ def common_arg_parser():
     parser.add_argument('--num_env', help='Number of environment copies being run in parallel. When not specified, set to number of cpus for Atari, and to 1 for Mujoco', default=None, type=int)
     parser.add_argument('--reward_scale', help='Reward scale factor. Default: 1.0', default=1.0, type=float)
     parser.add_argument('--save_path', help='Path to save trained model to', default=None, type=str)
-    parser.add_argument('--save_video_interval', help='Save video every x steps (0 = disabled)', default=0, type=int)
-    parser.add_argument('--save_video_length', help='Length of recorded video. Default: 200', default=200, type=int)
     parser.add_argument('--play', default=False, action='store_true')
     return parser
 

baselines/common/plot_util.py

@@ -1,401 +0,0 @@
-import matplotlib.pyplot as plt
-import os.path as osp
-import json
-import os
-import numpy as np
-import pandas
-from collections import defaultdict, namedtuple
-from baselines.bench import monitor
-from baselines.logger import read_json, read_csv
-
-def smooth(y, radius, mode='two_sided', valid_only=False):
-    '''
-    Smooth signal y, where radius is determines the size of the window
-
-    mode='twosided':
-        average over the window [max(index - radius, 0), min(index + radius, len(y)-1)]
-    mode='causal':
-        average over the window [max(index - radius, 0), index]
-
-    valid_only: put nan in entries where the full-sized window is not available
-
-    '''
-    assert mode in ('two_sided', 'causal')
-    if len(y) < 2*radius+1:
-        return np.ones_like(y) * y.mean()
-    elif mode == 'two_sided':
-        convkernel = np.ones(2 * radius+1)
-        out = np.convolve(y, convkernel,mode='same') / np.convolve(np.ones_like(y), convkernel, mode='same')
-        if valid_only:
-            out[:radius] = out[-radius:] = np.nan
-    elif mode == 'causal':
-        convkernel = np.ones(radius)
-        out = np.convolve(y, convkernel,mode='full') / np.convolve(np.ones_like(y), convkernel, mode='full')
-        out = out[:-radius+1]
-        if valid_only:
-            out[:radius] = np.nan
-    return out
-
-def one_sided_ema(xolds, yolds, low=None, high=None, n=512, decay_steps=1., low_counts_threshold=1e-8):
-    '''
-    perform one-sided (causal) EMA (exponential moving average)
-    smoothing and resampling to an even grid with n points.
-    Does not do extrapolation, so we assume
-    xolds[0] <= low && high <= xolds[-1]
-
-    Arguments:
-
-    xolds: array or list  - x values of data. Needs to be sorted in ascending order
-    yolds: array of list  - y values of data. Has to have the same length as xolds
-
-    low: float            - min value of the new x grid. By default equals to xolds[0]
-    high: float           - max value of the new x grid. By default equals to xolds[-1]
-
-    n: int                - number of points in new x grid
-
-    decay_steps: float    - EMA decay factor, expressed in new x grid steps.
-
-    low_counts_threshold: float or int
-                          - y values with counts less than this value will be set to NaN
-
-    Returns:
-        tuple sum_ys, count_ys where
-            xs        - array with new x grid
-            ys        - array of EMA of y at each point of the new x grid
-            count_ys  - array of EMA of y counts at each point of the new x grid
-
-    '''
-
-    low = xolds[0] if low is None else low
-    high = xolds[-1] if high is None else high
-
-    assert xolds[0] <= low, 'low = {} < xolds[0] = {} - extrapolation not permitted!'.format(low, xolds[0])
-    assert xolds[-1] >= high, 'high = {} > xolds[-1] = {}  - extrapolation not permitted!'.format(high, xolds[-1])
-    assert len(xolds) == len(yolds), 'length of xolds ({}) and yolds ({}) do not match!'.format(len(xolds), len(yolds))
-
-
-    xolds = xolds.astype('float64')
-    yolds = yolds.astype('float64')
-
-    luoi = 0 # last unused old index
-    sum_y = 0.
-    count_y = 0.
-    xnews = np.linspace(low, high, n)
-    decay_period = (high - low) / (n - 1) * decay_steps
-    interstep_decay = np.exp(- 1. / decay_steps)
-    sum_ys = np.zeros_like(xnews)
-    count_ys = np.zeros_like(xnews)
-    for i in range(n):
-        xnew = xnews[i]
-        sum_y *= interstep_decay
-        count_y *= interstep_decay
-        while True:
-            xold = xolds[luoi]
-            if xold <= xnew:
-                decay = np.exp(- (xnew - xold) / decay_period)
-                sum_y += decay * yolds[luoi]
-                count_y += decay
-                luoi += 1
-            else:
-                break
-            if luoi >= len(xolds):
-                break
-        sum_ys[i] = sum_y
-        count_ys[i] = count_y
-
-    ys = sum_ys / count_ys
-    ys[count_ys < low_counts_threshold] = np.nan
-
-    return xnews, ys, count_ys
-
-def symmetric_ema(xolds, yolds, low=None, high=None, n=512, decay_steps=1., low_counts_threshold=1e-8):
-    '''
-    perform symmetric EMA (exponential moving average)
-    smoothing and resampling to an even grid with n points.
-    Does not do extrapolation, so we assume
-    xolds[0] <= low && high <= xolds[-1]
-
-    Arguments:
-
-    xolds: array or list  - x values of data. Needs to be sorted in ascending order
-    yolds: array of list  - y values of data. Has to have the same length as xolds
-
-    low: float            - min value of the new x grid. By default equals to xolds[0]
-    high: float           - max value of the new x grid. By default equals to xolds[-1]
-
-    n: int                - number of points in new x grid
-
-    decay_steps: float    - EMA decay factor, expressed in new x grid steps.
-
-    low_counts_threshold: float or int
-                          - y values with counts less than this value will be set to NaN
-
-    Returns:
-        tuple sum_ys, count_ys where
-            xs        - array with new x grid
-            ys        - array of EMA of y at each point of the new x grid
-            count_ys  - array of EMA of y counts at each point of the new x grid
-
-    '''
-    xs, ys1, count_ys1 = one_sided_ema(xolds, yolds, low, high, n, decay_steps, low_counts_threshold=0)
-    _,  ys2, count_ys2 = one_sided_ema(-xolds[::-1], yolds[::-1], -high, -low, n, decay_steps, low_counts_threshold=0)
-    ys2 = ys2[::-1]
-    count_ys2 = count_ys2[::-1]
-    count_ys = count_ys1 + count_ys2
-    ys = (ys1 * count_ys1 + ys2 * count_ys2) / count_ys
-    ys[count_ys < low_counts_threshold] = np.nan
-    return xs, ys, count_ys
-
-Result = namedtuple('Result', 'monitor progress dirname metadata')
-Result.__new__.__defaults__ = (None,) * len(Result._fields)
-
-def load_results(root_dir_or_dirs, enable_progress=True, enable_monitor=True, verbose=False):
-    '''
-    load summaries of runs from a list of directories (including subdirectories)
-    Arguments:
-
-    enable_progress: bool - if True, will attempt to load data from progress.csv files (data saved by logger). Default: True
-
-    enable_monitor: bool - if True, will attempt to load data from monitor.csv files (data saved by Monitor environment wrapper). Default: True
-
-    verbose: bool - if True, will print out list of directories from which the data is loaded. Default: False
-
-
-    Returns:
-    List of Result objects with the following fields:
-         - dirname - path to the directory data was loaded from
-         - metadata - run metadata (such as command-line arguments and anything else in metadata.json file
-         - monitor - if enable_monitor is True, this field contains pandas dataframe with loaded monitor.csv file (or aggregate of all *.monitor.csv files in the directory)
-         - progress - if enable_progress is True, this field contains pandas dataframe with loaded progress.csv file
-    '''
-    if isinstance(root_dir_or_dirs, str):
-        rootdirs = [osp.expanduser(root_dir_or_dirs)]
-    else:
-        rootdirs = [osp.expanduser(d) for d in root_dir_or_dirs]
-    allresults = []
-    for rootdir in rootdirs:
-        assert osp.exists(rootdir), "%s doesn't exist"%rootdir
-        for dirname, dirs, files in os.walk(rootdir):
-            if '-proc' in dirname:
-                files[:] = []
-                continue
-            if set(['metadata.json', 'monitor.json', 'monitor.csv', 'progress.json', 'progress.csv']).intersection(files):
-                # used to be uncommented, which means do not go deeper than current directory if any of the data files
-                # are found
-                # dirs[:] = []
-                result = {'dirname' : dirname}
-                if "metadata.json" in files:
-                    with open(osp.join(dirname, "metadata.json"), "r") as fh:
-                        result['metadata'] = json.load(fh)
-                progjson = osp.join(dirname, "progress.json")
-                progcsv = osp.join(dirname, "progress.csv")
-                if enable_progress:
-                    if osp.exists(progjson):
-                        result['progress'] = pandas.DataFrame(read_json(progjson))
-                    elif osp.exists(progcsv):
-                        try:
-                            result['progress'] = read_csv(progcsv)
-                        except pandas.errors.EmptyDataError:
-                            print('skipping progress file in ', dirname, 'empty data')
-                    else:
-                        if verbose: print('skipping %s: no progress file'%dirname)
-
-                if enable_monitor:
-                    try:
-                        result['monitor'] = pandas.DataFrame(monitor.load_results(dirname))
-                    except monitor.LoadMonitorResultsError:
-                        print('skipping %s: no monitor files'%dirname)
-                    except Exception as e:
-                        print('exception loading monitor file in %s: %s'%(dirname, e))
-
-                if result.get('monitor') is not None or result.get('progress') is not None:
-                    allresults.append(Result(**result))
-                    if verbose:
-                        print('successfully loaded %s'%dirname)
-
-    if verbose: print('loaded %i results'%len(allresults))
-    return allresults
-
-COLORS = ['blue', 'green', 'red', 'cyan', 'magenta', 'yellow', 'black', 'purple', 'pink',
-        'brown', 'orange', 'teal',  'lightblue', 'lime', 'lavender', 'turquoise',
-        'darkgreen', 'tan', 'salmon', 'gold',  'darkred', 'darkblue']
-
-
-def default_xy_fn(r):
-    x = np.cumsum(r.monitor.l)
-    y = smooth(r.monitor.r, radius=10)
-    return x,y
-
-def default_split_fn(r):
-    import re
-    # match name between slash and -<digits> at the end of the string
-    # (slash in the beginning or -<digits> in the end or either may be missing)
-    match = re.search(r'[^/-]+(?=(-\d+)?\Z)', r.dirname)
-    if match:
-        return match.group(0)
-
-def plot_results(
-    allresults, *,
-    xy_fn=default_xy_fn,
-    split_fn=default_split_fn,
-    group_fn=default_split_fn,
-    average_group=False,
-    shaded_std=True,
-    shaded_err=True,
-    figsize=None,
-    legend_outside=False,
-    resample=0,
-    smooth_step=1.0,
-):
-    '''
-    Plot multiple Results objects
-
-    xy_fn: function Result -> x,y           - function that converts results objects into tuple of x and y values.
-                                              By default, x is cumsum of episode lengths, and y is episode rewards
-
-    split_fn: function Result -> hashable   - function that converts results objects into keys to split curves into sub-panels by.
-                                              That is, the results r for which split_fn(r) is different will be put on different sub-panels.
-                                              By default, the portion of r.dirname between last / and -<digits> is returned. The sub-panels are
-                                              stacked vertically in the figure.
-
-    group_fn: function Result -> hashable   - function that converts results objects into keys to group curves by.
-                                              That is, the results r for which group_fn(r) is the same will be put into the same group.
-                                              Curves in the same group have the same color (if average_group is False), or averaged over
-                                              (if average_group is True). The default value is the same as default value for split_fn
-
-    average_group: bool                     - if True, will average the curves in the same group and plot the mean. Enables resampling
-                                              (if resample = 0, will use 512 steps)
-
-    shaded_std: bool                        - if True (default), the shaded region corresponding to standard deviation of the group of curves will be
-                                              shown (only applicable if average_group = True)
-
-    shaded_err: bool                        - if True (default), the shaded region corresponding to error in mean estimate of the group of curves
-                                              (that is, standard deviation divided by square root of number of curves) will be
-                                              shown (only applicable if average_group = True)
-
-    figsize: tuple or None                  - size of the resulting figure (including sub-panels). By default, width is 6 and height is 6 times number of
-                                              sub-panels.
-
-
-    legend_outside: bool                    - if True, will place the legend outside of the sub-panels.
-
-    resample: int                           - if not zero, size of the uniform grid in x direction to resample onto. Resampling is performed via symmetric
-                                              EMA smoothing (see the docstring for symmetric_ema).
-                                              Default is zero (no resampling). Note that if average_group is True, resampling is necessary; in that case, default
-                                              value is 512.
-
-    smooth_step: float                      - when resampling (i.e. when resample > 0 or average_group is True), use this EMA decay parameter (in units of the new grid step).
-                                              See docstrings for decay_steps in symmetric_ema or one_sided_ema functions.
-
-    '''
-
-    if split_fn is None: split_fn = lambda _ : ''
-    if group_fn is None: group_fn = lambda _ : ''
-    sk2r = defaultdict(list) # splitkey2results
-    for result in allresults:
-        splitkey = split_fn(result)
-        sk2r[splitkey].append(result)
-    assert len(sk2r) > 0
-    assert isinstance(resample, int), "0: don't resample. <integer>: that many samples"
-    nrows = len(sk2r)
-    ncols = 1
-    figsize = figsize or (6, 6 * nrows)
-    f, axarr = plt.subplots(nrows, ncols, sharex=False, squeeze=False, figsize=figsize)
-
-    groups = list(set(group_fn(result) for result in allresults))
-
-    default_samples = 512
-    if average_group:
-        resample = resample or default_samples
-
-    for (isplit, sk) in enumerate(sorted(sk2r.keys())):
-        g2l = {}
-        g2c = defaultdict(int)
-        sresults = sk2r[sk]
-        gresults = defaultdict(list)
-        ax = axarr[isplit][0]
-        for result in sresults:
-            group = group_fn(result)
-            g2c[group] += 1
-            x, y = xy_fn(result)
-            if x is None: x = np.arange(len(y))
-            x, y = map(np.asarray, (x, y))
-            if average_group:
-                gresults[group].append((x,y))
-            else:
-                if resample:
-                    x, y, counts = symmetric_ema(x, y, x[0], x[-1], resample, decay_steps=smooth_step)
-                l, = ax.plot(x, y, color=COLORS[groups.index(group) % len(COLORS)])
-                g2l[group] = l
-        if average_group:
-            for group in sorted(groups):
-                xys = gresults[group]
-                if not any(xys):
-                    continue
-                color = COLORS[groups.index(group) % len(COLORS)]
-                origxs = [xy[0] for xy in xys]
-                minxlen = min(map(len, origxs))
-                def allequal(qs):
-                    return all((q==qs[0]).all() for q in qs[1:])
-                if resample:
-                    low  = max(x[0] for x in origxs)
-                    high = min(x[-1] for x in origxs)
-                    usex = np.linspace(low, high, resample)
-                    ys = []
-                    for (x, y) in xys:
-                        ys.append(symmetric_ema(x, y, low, high, resample, decay_steps=smooth_step)[1])
-                else:
-                    assert allequal([x[:minxlen] for x in origxs]),\
-                        'If you want to average unevenly sampled data, set resample=<number of samples you want>'
-                    usex = origxs[0]
-                    ys = [xy[1][:minxlen] for xy in xys]
-                ymean = np.mean(ys, axis=0)
-                ystd = np.std(ys, axis=0)
-                ystderr = ystd / np.sqrt(len(ys))
-                l, = axarr[isplit][0].plot(usex, ymean, color=color)
-                g2l[group] = l
-                if shaded_err:
-                    ax.fill_between(usex, ymean - ystderr, ymean + ystderr, color=color, alpha=.4)
-                if shaded_std:
-                    ax.fill_between(usex, ymean - ystd,    ymean + ystd,    color=color, alpha=.2)
-
-
-        # https://matplotlib.org/users/legend_guide.html
-        plt.tight_layout()
-        if any(g2l.keys()):
-            ax.legend(
-                g2l.values(),
-                ['%s (%i)'%(g, g2c[g]) for g in g2l] if average_group else g2l.keys(),
-                loc=2 if legend_outside else None,
-                bbox_to_anchor=(1,1) if legend_outside else None)
-        ax.set_title(sk)
-    return f, axarr
-
-def regression_analysis(df):
-    xcols = list(df.columns.copy())
-    xcols.remove('score')
-    ycols = ['score']
-    import statsmodels.api as sm
-    mod = sm.OLS(df[ycols], sm.add_constant(df[xcols]), hasconst=False)
-    res = mod.fit()
-    print(res.summary())
-
-def test_smooth():
-    norig = 100
-    nup = 300
-    ndown = 30
-    xs = np.cumsum(np.random.rand(norig) * 10 / norig)
-    yclean = np.sin(xs)
-    ys = yclean + .1 * np.random.randn(yclean.size)
-    xup, yup, _ = symmetric_ema(xs, ys, xs.min(), xs.max(), nup, decay_steps=nup/ndown)
-    xdown, ydown, _ = symmetric_ema(xs, ys, xs.min(), xs.max(), ndown, decay_steps=ndown/ndown)
-    xsame, ysame, _ = symmetric_ema(xs, ys, xs.min(), xs.max(), norig, decay_steps=norig/ndown)
-    plt.plot(xs, ys, label='orig', marker='x')
-    plt.plot(xup, yup, label='up', marker='x')
-    plt.plot(xdown, ydown, label='down', marker='x')
-    plt.plot(xsame, ysame, label='same', marker='x')
-    plt.plot(xs, yclean, label='clean', marker='x')
-    plt.legend()
-    plt.show()
-
-

baselines/common/vec_env/__init__.py

@@ -32,11 +32,6 @@ class VecEnv(ABC):
     """
     closed = False
     viewer = None
-
-    metadata = {
-        'render.modes': ['human', 'rgb_array']
-    }
-
     def __init__(self, num_envs, observation_space, action_space):
         self.num_envs = num_envs
         self.observation_space = observation_space

baselines/common/vec_env/dummy_vec_env.py

@@ -20,6 +20,9 @@ class DummyVecEnv(VecEnv):
         env = self.envs[0]
         VecEnv.__init__(self, len(env_fns), env.observation_space, env.action_space)
         obs_space = env.observation_space
+        if isinstance(obs_space, spaces.MultiDiscrete):
+            obs_space.shape = obs_space.shape[0]
+
         self.keys, shapes, dtypes = obs_space_info(obs_space)
 
         self.buf_obs = { k: np.zeros((self.num_envs,) + tuple(shapes[k]), dtype=dtypes[k]) for k in self.keys }
@@ -76,6 +79,6 @@ class DummyVecEnv(VecEnv):
 
     def render(self, mode='human'):
         if self.num_envs == 1:
-            return self.envs[0].render(mode=mode)
+            self.envs[0].render(mode=mode)
         else:
-            return super().render(mode=mode)
+            super().render(mode=mode)

baselines/common/vec_env/test_video_recorder.py

@@ -1,49 +0,0 @@
-"""
-Tests for asynchronous vectorized environments.
-"""
-
-import gym
-import pytest
-import os
-import glob
-import tempfile
-
-from .dummy_vec_env import DummyVecEnv
-from .shmem_vec_env import ShmemVecEnv
-from .subproc_vec_env import SubprocVecEnv
-from .vec_video_recorder import VecVideoRecorder
-
-@pytest.mark.parametrize('klass', (DummyVecEnv, ShmemVecEnv, SubprocVecEnv))
-@pytest.mark.parametrize('num_envs', (1, 4))
-@pytest.mark.parametrize('video_length', (10, 100))
-@pytest.mark.parametrize('video_interval', (1, 50))
-def test_video_recorder(klass, num_envs, video_length, video_interval):
-    """
-    Wrap an existing VecEnv with VevVideoRecorder,
-    Make (video_interval + video_length + 1) steps,
-    then check that the file is present
-    """
-
-    def make_fn():
-        env = gym.make('PongNoFrameskip-v4')
-        return env
-    fns = [make_fn for _ in range(num_envs)]
-    env = klass(fns)
-
-    with tempfile.TemporaryDirectory() as video_path:
-        env = VecVideoRecorder(env, video_path, record_video_trigger=lambda x: x % video_interval == 0, video_length=video_length)
-
-        env.reset()
-        for _ in range(video_interval + video_length + 1):
-            env.step([0] * num_envs)
-        env.close()
-
-
-        recorded_video = glob.glob(os.path.join(video_path, "*.mp4"))
-
-        # first and second step
-        assert len(recorded_video) == 2
-        # Files are not empty
-        assert all(os.stat(p).st_size != 0 for p in recorded_video)
-
-

baselines/common/vec_env/vec_video_recorder.py

@@ -1,89 +0,0 @@
-import os
-from baselines import logger
-from baselines.common.vec_env import VecEnvWrapper
-from gym.wrappers.monitoring import video_recorder
-
-
-class VecVideoRecorder(VecEnvWrapper):
-    """
-    Wrap VecEnv to record rendered image as mp4 video.
-    """
-
-    def __init__(self, venv, directory, record_video_trigger, video_length=200):
-        """
-        # Arguments
-            venv: VecEnv to wrap
-            directory: Where to save videos
-            record_video_trigger:
-                Function that defines when to start recording.
-                The function takes the current number of step,
-                and returns whether we should start recording or not.
-            video_length: Length of recorded video
-        """
-
-        VecEnvWrapper.__init__(self, venv)
-        self.record_video_trigger = record_video_trigger
-        self.video_recorder = None
-
-        self.directory = os.path.abspath(directory)
-        if not os.path.exists(self.directory): os.mkdir(self.directory)
-
-        self.file_prefix = "vecenv"
-        self.file_infix = '{}'.format(os.getpid())
-        self.step_id = 0
-        self.video_length = video_length
-
-        self.recording = False
-        self.recorded_frames = 0
-
-    def reset(self):
-        obs = self.venv.reset()
-
-        self.start_video_recorder()
-
-        return obs
-
-    def start_video_recorder(self):
-        self.close_video_recorder()
-
-        base_path = os.path.join(self.directory, '{}.video.{}.video{:06}'.format(self.file_prefix, self.file_infix, self.step_id))
-        self.video_recorder = video_recorder.VideoRecorder(
-                env=self.venv,
-                base_path=base_path,
-                metadata={'step_id': self.step_id}
-                )
-
-        self.video_recorder.capture_frame()
-        self.recorded_frames = 1
-        self.recording = True
-
-    def _video_enabled(self):
-        return self.record_video_trigger(self.step_id)
-
-    def step_wait(self):
-        obs, rews, dones, infos = self.venv.step_wait()
-
-        self.step_id += 1
-        if self.recording:
-            self.video_recorder.capture_frame()
-            self.recorded_frames += 1
-            if self.recorded_frames > self.video_length:
-                logger.info("Saving video to ", self.video_recorder.path)
-                self.close_video_recorder()
-        elif self._video_enabled():
-                self.start_video_recorder()
-
-        return obs, rews, dones, infos
-
-    def close_video_recorder(self):
-        if self.recording:
-            self.video_recorder.close()
-        self.recording = False
-        self.recorded_frames = 0
-
-    def close(self):
-        VecEnvWrapper.close(self)
-        self.close_video_recorder()
-
-    def __del__(self):
-        self.close()

baselines/ddpg/ddpg.py

@@ -66,6 +66,7 @@ def learn(network, env,
 
     action_noise = None
     param_noise = None
+    nb_actions = env.action_space.shape[-1]
     if noise_type is not None:
         for current_noise_type in noise_type.split(','):
             current_noise_type = current_noise_type.strip()

baselines/ddpg/ddpg_learner.py

@@ -271,7 +271,7 @@ class DDPG(object):
 
         if self.action_noise is not None and apply_noise:
             noise = self.action_noise()
-            assert noise.shape == action[0].shape
+            assert noise.shape == action.shape
             action += noise
         action = np.clip(action, self.action_range[0], self.action_range[1])
 

baselines/ppo2/defaults.py

@@ -20,6 +20,3 @@ def atari():
         lr=lambda f : f * 2.5e-4,
         cliprange=lambda f : f * 0.1,
     )
-
-def retro():
-    return atari()

baselines/run.py

@@ -6,7 +6,6 @@ from collections import defaultdict
 import tensorflow as tf
 import numpy as np
 
-from baselines.common.vec_env.vec_video_recorder import VecVideoRecorder
 from baselines.common.vec_env.vec_frame_stack import VecFrameStack
 from baselines.common.cmd_util import common_arg_parser, parse_unknown_args, make_vec_env, make_env
 from baselines.common.tf_util import get_session
@@ -63,8 +62,6 @@ def train(args, extra_args):
     alg_kwargs.update(extra_args)
 
     env = build_env(args)
-    if args.save_video_interval != 0:
-        env = VecVideoRecorder(env, osp.join(logger.Logger.CURRENT.dir, "videos"), record_video_trigger=lambda x: x % args.save_video_interval == 0, video_length=args.save_video_length)
 
     if args.network:
         alg_kwargs['network'] = args.network

setup.py

@@ -11,7 +11,6 @@ extras = {
     'test': [
         'filelock',
         'pytest',
-        'pytest-forked',
         'atari-py'
     ],
     'bullet': [

test.dockerfile.py36-mpi

@@ -0,0 +1,16 @@
+FROM python:3.6
+
+RUN apt-get -y update && apt-get -y install ffmpeg libopenmpi-dev
+ENV CODE_DIR /root/code
+
+COPY . $CODE_DIR/baselines
+WORKDIR $CODE_DIR/baselines
+
+# Clean up pycache and pyc files
+RUN rm -rf __pycache__ && \
+    find . -name "*.pyc" -delete && \
+    pip install tensorflow && \
+    pip install -e .[test,mpi]
+
+
+CMD /bin/bash

test.dockerfile.py36-nompi

@@ -1,8 +1,6 @@
 FROM python:3.6
 
 RUN apt-get -y update && apt-get -y install ffmpeg
-# RUN apt-get -y update && apt-get -y install git wget python-dev python3-dev libopenmpi-dev python-pip zlib1g-dev cmake python-opencv
-
 ENV CODE_DIR /root/code
 
 COPY . $CODE_DIR/baselines