diff --git a/Report.md b/Report.md
new file mode 100644
index 0000000..01c8529
--- /dev/null
+++ b/Report.md
@@ -0,0 +1,63 @@
+# Project: Navigation
+
+This is a project that uses Deep Q-Networks to train an agent to capture yellow bananas and avoid
+blue bananas through deep reinforcement learning in a Unity ML-Agents environment.
+
+## Learning algorithm
+
+Q-Learning is an approach which generates a Q-table that is used by an agent to determine best action
+for a given state. This technique becomes difficult and inefficient in environments that have a large
+state space. Deep Q-Networks on the other hand makes use of a neural network to approximate Q-values
+for each action based on the input state.
+
+However, there are drawbacks in Deep Q-Learning. A common issue is that the reinforcement learning tends
+to be unstable or divergent when a non-linear function approximator such as neural networks are used to
+represent Q. This instability comes from the correlations present in the sequence of observations, the fact
+that small updates to Q may significantly change the policy and the data distribution, and the correlations
+between Q and the target values. [1]
+
+To overcome this, experience replay is a technique that was used in this solution that uses the biologically
+inspired approach of replaying a random sample of prior actions to remove correlations in the observation
+sequence and smooth changes in the data distribution.
+
+## Model architecture and hyperparameters
+
+Fully connected layer 1: Input 37 (state space), Output 32, RELU
+Fully connected layer 2: Input 32, Output 32, RELU
+Fully connected layer 3: Input 32, Output 4 (action space)
+
+The hyperparameters for tweaking and optimizing the learning algorithm were:
+
+max_t (750): maximum number of timesteps per episode
+eps_start (1.0): starting value of epsilon, for epsilon-greedy action selection
+eps_end (0.01): minimum value of epsilon
+eps_decay (0.9): multiplacative factor (per episode) for decreasing epsilon
+
+## Plot of rewards
+
+Below is a training run of the above model architecture and hyperparameters:
+
+```
+Number of agents: 1
+Number of actions: 4
+Episode 100	Average Score: 3.97
+Episode 200	Average Score: 9.51
+Episode 287	Average Score: 13.12
+Environment solved in 187 episodes!	Average Score: 13.12
+```
+
+The plot of rewards for this run is as follows:
+
+![Plot of rewards](https://raw.githubusercontent.com/aweeraman/deep-q-networks-navigation/master/graph.png)
+
+## Ideas for future work
+
+Further optimization of this architecture can be performed by training with different hyperparameters to
+get faster and better learning outcomes. A couple of further approaches to try out are:
+
+* Double Q-Learning
+* Delayed Q-Learning
+
+## Reference
+
+1 - https://en.wikipedia.org/wiki/Q-learning#Deep_Q-learning
diff --git a/bananas.py b/bananas.py
index 5f684ee..c15d574 100644
--- a/bananas.py
+++ b/bananas.py
@@ -9,7 +9,7 @@
 import matplotlib.pyplot as plt
 
 # https://github.com/udacity/deep-reinforcement-learning/blob/master/dqn/solution/Deep_Q_Network_Solution.ipynb
-def dqn(n_episodes=1000, max_t=750, eps_start=1.0, eps_end=0.01, eps_decay=0.995):
+def dqn(n_episodes=1000, max_t=750, eps_start=1.0, eps_end=0.01, eps_decay=0.9):
     """Deep Q-Learning.
 
     Params
diff --git a/weights.pth b/weights.pth
index af70317..4693f74 100644
Binary files a/weights.pth and b/weights.pth differ