Add random initial action in ddpg (#13)

* Add random initial actions in ddpg

* Add reacher-v2 example of ddpg

scripts/algorithms/common/noise.py

@@ -1,5 +1,5 @@
 # -*- coding: utf-8 -*-
-"""Noise classes for baselines."""
+"""Noise classes for algorithms."""
 
 import copy
 import random
@@ -7,6 +7,31 @@ import random
 import numpy as np
 
 
+class GaussianNoise:
+    """Gaussian Noise.
+
+    Taken from https://github.com/vitchyr/rlkit
+    """
+
+    def __init__(
+        self,
+        min_sigma: float = 1.0,
+        max_sigma: float = 1.0,
+        decay_period: int = 1000000,
+    ):
+        """Initialization."""
+        self.max_sigma = max_sigma
+        self.min_sigma = min_sigma
+        self.decay_period = decay_period
+
+    def sample(self, action_size: int, t: int = 0) -> float:
+        """Get an action with gaussian noise."""
+        sigma = self.max_sigma - (self.max_sigma - self.min_sigma) * min(
+            1.0, t / self.decay_period
+        )
+        return np.random.normal(0, sigma, size=action_size)
+
+
 class OUNoise:
     """Ornstein-Uhlenbeck process.
 
@@ -15,7 +40,9 @@ class OUNoise:
     ddpg-pendulum/ddpg_agent.py
     """
 
-    def __init__(self, size, mu=0.0, theta=0.15, sigma=0.2):
+    def __init__(
+        self, size: int, mu: float = 0.0, theta: float = 0.15, sigma: float = 0.2
+    ):
         """Initialize parameters and noise process."""
         self.state = np.float64(0.0)
         self.mu = mu * np.ones(size)
@@ -27,7 +54,7 @@ class OUNoise:
         """Reset the internal state (= noise) to mean (mu)."""
         self.state = copy.copy(self.mu)
 
-    def sample(self):
+    def sample(self) -> float:
         """Update internal state and return it as a noise sample."""
         x = self.state
         dx = self.theta * (self.mu - x) + self.sigma * np.array(