--- a
+++ b/baselines/results_plotter.py
@@ -0,0 +1,87 @@
+import numpy as np
+import matplotlib
+matplotlib.use('TkAgg') # Can change to 'Agg' for non-interactive mode
+
+import matplotlib.pyplot as plt
+plt.rcParams['svg.fonttype'] = 'none'
+
+from baselines.bench.monitor import load_results
+
+X_TIMESTEPS = 'timesteps'
+X_EPISODES = 'episodes'
+X_WALLTIME = 'walltime_hrs'
+POSSIBLE_X_AXES = [X_TIMESTEPS, X_EPISODES, X_WALLTIME]
+EPISODES_WINDOW = 100
+COLORS = ['blue', 'green', 'red', 'cyan', 'magenta', 'yellow', 'black', 'purple', 'pink',
+        'brown', 'orange', 'teal', 'coral', 'lightblue', 'lime', 'lavender', 'turquoise',
+        'darkgreen', 'tan', 'salmon', 'gold', 'lightpurple', 'darkred', 'darkblue']
+
+def rolling_window(a, window):
+    shape = a.shape[:-1] + (a.shape[-1] - window + 1, window)
+    strides = a.strides + (a.strides[-1],)
+    return np.lib.stride_tricks.as_strided(a, shape=shape, strides=strides)
+
+def window_func(x, y, window, func):
+    yw = rolling_window(y, window)
+    yw_func = func(yw, axis=-1)
+    return x[window-1:], yw_func
+
+def ts2xy(ts, xaxis):
+    if xaxis == X_TIMESTEPS:
+        x = np.cumsum(ts.l.values)
+        y = ts.r.values
+    elif xaxis == X_EPISODES:
+        x = np.arange(len(ts))
+        y = ts.r.values
+    elif xaxis == X_WALLTIME:
+        x = ts.t.values / 3600.
+        y = ts.r.values
+    else:
+        raise NotImplementedError
+    return x, y
+
+def plot_curves(xy_list, xaxis, title):
+    plt.figure(figsize=(8,2))
+    maxx = max(xy[0][-1] for xy in xy_list)
+    minx = 0
+    for (i, (x, y)) in enumerate(xy_list):
+        color = COLORS[i]
+        plt.scatter(x, y, s=2)
+        x, y_mean = window_func(x, y, EPISODES_WINDOW, np.mean) #So returns average of last EPISODE_WINDOW episodes
+        plt.plot(x, y_mean, color=color)
+    plt.xlim(minx, maxx)
+    plt.title(title)
+    plt.xlabel(xaxis)
+    plt.ylabel("Episode Rewards")
+    plt.tight_layout()
+
+def plot_results(dirs, num_timesteps, xaxis, task_name):
+    tslist = []
+    for dir in dirs:
+        ts = load_results(dir)
+        ts = ts[ts.l.cumsum() <= num_timesteps]
+        tslist.append(ts)
+    xy_list = [ts2xy(ts, xaxis) for ts in tslist]
+    plot_curves(xy_list, xaxis, task_name)
+
+# Example usage in jupyter-notebook
+# from baselines import log_viewer
+# %matplotlib inline
+# log_viewer.plot_results(["./log"], 10e6, log_viewer.X_TIMESTEPS, "Breakout")
+# Here ./log is a directory containing the monitor.csv files
+
+def main():
+    import argparse
+    import os
+    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+    parser.add_argument('--dirs', help='List of log directories', nargs = '*', default=['./log'])
+    parser.add_argument('--num_timesteps', type=int, default=int(10e6))
+    parser.add_argument('--xaxis', help = 'Varible on X-axis', default = X_TIMESTEPS)
+    parser.add_argument('--task_name', help = 'Title of plot', default = 'Breakout')
+    args = parser.parse_args()
+    args.dirs = [os.path.abspath(dir) for dir in args.dirs]
+    plot_results(args.dirs, args.num_timesteps, args.xaxis, args.task_name)
+    plt.show()
+
+if __name__ == '__main__':
+    main()
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