diff --git a/.coveragerc b/.coveragerc
index a8fc2af..511f20d 100644
--- a/.coveragerc
+++ b/.coveragerc
@@ -3,6 +3,8 @@ branch = False
 omit =
     tests/*
     setup.py
+    # Require graphical interface
+    torchy_baselines/common/results_plotter.py
 
 [report]
 exclude_lines =
diff --git a/docs/misc/changelog.rst b/docs/misc/changelog.rst
index 0bb3910..b083e39 100644
--- a/docs/misc/changelog.rst
+++ b/docs/misc/changelog.rst
@@ -20,6 +20,7 @@ New Features:
 - Add methods for saving and loading replay buffer
 - Add `extend()` method to the buffers
 - Add `get_vec_normalize_env()` to `BaseRLModel` to retrieve `VecNormalize` wrapper when it exists
+- Add `¶results_plotter` from Stable Baselines
 
 Bug Fixes:
 ^^^^^^^^^^
diff --git a/torchy_baselines/common/results_plotter.py b/torchy_baselines/common/results_plotter.py
new file mode 100644
index 0000000..a5464da
--- /dev/null
+++ b/torchy_baselines/common/results_plotter.py
@@ -0,0 +1,126 @@
+from typing import Tuple, Callable, List, Optional
+
+import numpy as np
+import pandas as pd
+import matplotlib
+import matplotlib.pyplot as plt
+
+from torchy_baselines.common.monitor import load_results
+
+# matplotlib.use('TkAgg')  # Can change to 'Agg' for non-interactive mode
+plt.rcParams['svg.fonttype'] = 'none'
+
+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(array: np.ndarray, window: int) -> np.ndarray:
+    """
+    Apply a rolling window to a np.ndarray
+
+    :param array: (np.ndarray) the input Array
+    :param window: (int) length of the rolling window
+    :return: (np.ndarray) rolling window on the input array
+    """
+    shape = array.shape[:-1] + (array.shape[-1] - window + 1, window)
+    strides = array.strides + (array.strides[-1],)
+    return np.lib.stride_tricks.as_strided(array, shape=shape, strides=strides)
+
+
+def window_func(var_1: np.ndarray, var_2: np.ndarray,
+                window: int, func: Callable) -> Tuple[np.ndarray, np.ndarray]:
+    """
+    Apply a function to the rolling window of 2 arrays
+
+    :param var_1: (np.ndarray) variable 1
+    :param var_2: (np.ndarray) variable 2
+    :param window: (int) length of the rolling window
+    :param func: (numpy function) function to apply on the rolling window on variable 2 (such as np.mean)
+    :return: (Tuple[np.ndarray, np.ndarray])  the rolling output with applied function
+    """
+    var_2_window = rolling_window(var_2, window)
+    function_on_var2 = func(var_2_window, axis=-1)
+    return var_1[window - 1:], function_on_var2
+
+
+def ts2xy(timesteps: pd.DataFrame, x_axis: str) -> Tuple[np.ndarray, np.ndarray]:
+    """
+    Decompose a timesteps variable to x ans ys
+
+    :param timesteps: (pd.DataFrame) the input data
+    :param x_axis: (str) the axis for the x and y output
+        (can be X_TIMESTEPS='timesteps', X_EPISODES='episodes' or X_WALLTIME='walltime_hrs')
+    :return: (Tuple[np.ndarray, np.ndarray]) the x and y output
+    """
+    if x_axis == X_TIMESTEPS:
+        x_var = np.cumsum(timesteps.l.values)
+        y_var = timesteps.r.values
+    elif x_axis == X_EPISODES:
+        x_var = np.arange(len(timesteps))
+        y_var = timesteps.r.values
+    elif x_axis == X_WALLTIME:
+        # Convert to hours
+        x_var = timesteps.t.values / 3600.
+        y_var = timesteps.r.values
+    else:
+        raise NotImplementedError
+    return x_var, y_var
+
+
+def plot_curves(xy_list: List[Tuple[np.ndarray, np.ndarray]],
+                x_axis: str, title: str, figsize: Tuple[int, int] = (8, 2)) -> None:
+    """
+    plot the curves
+
+    :param xy_list: (List[Tuple[np.ndarray, np.ndarray]]) the x and y coordinates to plot
+    :param x_axis: (str) the axis for the x and y output
+        (can be X_TIMESTEPS='timesteps', X_EPISODES='episodes' or X_WALLTIME='walltime_hrs')
+    :param title: (str) the title of the plot
+    :param figsize: (Tuple[int, int]) Size of the figure (width, height)
+    """
+
+    plt.figure(figsize=figsize)
+    max_x = max(xy[0][-1] for xy in xy_list)
+    min_x = 0
+    for (i, (x, y)) in enumerate(xy_list):
+        color = COLORS[i]
+        plt.scatter(x, y, s=2)
+        # Do not plot the smoothed curve at all if the timeseries is shorter than window size.
+        if x.shape[0] >= EPISODES_WINDOW:
+            # Compute and plot rolling mean with window of size EPISODE_WINDOW
+            x, y_mean = window_func(x, y, EPISODES_WINDOW, np.mean)
+            plt.plot(x, y_mean, color=color)
+    plt.xlim(min_x, max_x)
+    plt.title(title)
+    plt.xlabel(x_axis)
+    plt.ylabel("Episode Rewards")
+    plt.tight_layout()
+
+
+def plot_results(dirs: List[str], num_timesteps: Optional[int],
+                 x_axis: str, task_name: str, figsize: Tuple[int, int] = (8, 2)) -> None:
+    """
+    plot the results
+
+    :param dirs: ([str]) the save location of the results to plot
+    :param num_timesteps: (int or None) only plot the points below this value
+    :param x_axis: (str) the axis for the x and y output
+        (can be X_TIMESTEPS='timesteps', X_EPISODES='episodes' or X_WALLTIME='walltime_hrs')
+    :param task_name: (str) the title of the task to plot
+    :param figsize: (Tuple[int, int]) Size of the figure (width, height)
+    """
+
+    timesteps_list = []
+    for folder in dirs:
+        timesteps = load_results(folder)
+        if num_timesteps is not None:
+            timesteps = timesteps[timesteps.l.cumsum() <= num_timesteps]
+        timesteps_list.append(timesteps)
+    xy_list = [ts2xy(timesteps_item, x_axis) for timesteps_item in timesteps_list]
+    plot_curves(xy_list, x_axis, task_name, figsize)