#26 Update documentation

Author: pockerman

README.md

@@ -1,24 +1,62 @@
 # RL anonymity
 
-An experimental effort to use reinforcement learning techniques for data anonymity. 
+An experimental effort to use reinforcement learning techniques for data anonymization. 
 
 ## Conceptual overview
 
+The term data anonymization refers to techiniques that can be applied on a given dataset, D, such that after
+the latter has been submitted to such techniques, it makes it difficult for a third party to identify or infer the existence
+of specific individuals in D. Anonymization techniques, typically result into some sort of distortion
+of the original dataset. This means that in order to maintain some utility of the transformed dataset, the transofrmations
+applied should be constrained in some sense. In the end, it can be argued, that data anonymization is an optimization problem
+meaning striking the right balance between data utility and privacy. 
+
 Reinforcement learning is a learning framework based on accumulated experience. In this paradigm, an agent is learning by iteracting with an environment 
-without (to a large extent) any supervision. The following image   schematically describes the reinforcement learning framework 
+without (to a large extent) any supervision. The following image describes, schematically, the reinforcement learning framework .
 
 ![RL paradigm](images/agent_environment_interface.png "Reinforcement learning paradigm") 
 
-The framework has been use successfully to many recent advances in corntol, robotics, games and elsewhere.
+The agent chooses an action, ```a_t```, to perform out of predefined set of actions ```A```. The chosen action is executed by the environment
+instance and returns to the agent a reward signal, ```r_t```, as well as the new state, ```s_t```, that the enviroment is in. 
+The framework has successfully been used  to many recent advances in control, robotics, games and elsewhere.
+
 
-Given that data anonymity is essentially an optimization problem; between data utility and privacy, in this repository we try
-to use the reinforcement learning paradigm in order to train agents to perform this optimization for us. The following image
-places this into a persepctive 
+Let's assume that we have in our disposal two numbers a minimum distortion, ```MIN_DIST``` that should be applied to the dataset
+for achieving privacy and a maximum distortion, ```MAX_DIST```,  that should be applied to the dataset in order to maintain some utility.
+Let's assume also that any overall dataset distortion in ```[MIN_DIST, MAX_DIST]``` is acceptable in order to cast the dataset as 
+preserving  privacy and preserving dataset utility. We can then train a reinforcement learning agent to distort the dataset
+such that the aforementioned objective is achieved.
 
+Overall, this is shown in the image below.
 
 ![RL anonymity paradigm](images/general_concept.png "Reinforcement learning anonymity schematics")
 
+The images below show the overall running distortion average and running reward average achieved by using the 
+<a href="https://en.wikipedia.org/wiki/Q-learning">Q-learning</a> algorithm and various policies.
+
+**Q-learning with epsilon-greedy policy and constant epsilon**
+![RL anonymity paradigm](images/q_learn_epsilon_greedy_avg_run_distortion.png "Epsilon-greedy constant epsilon ")
+![RL anonymity paradigm](images/q_learn_epsilon_greedy_avg_run_reward.png "Reinforcement learning anonymity schematics")
+
+**Q-learning with epsilon-greedy policy and decaying epsilon per episode**
+![RL anonymity paradigm](images/q_learn_epsilon_greedy_decay_avg_run_distortion.png "Reinforcement learning anonymity schematics")
+![RL anonymity paradigm](images/q_learn_epsilon_greedy_decay_avg_run_reward.png "Reinforcement learning anonymity schematics")
+
+
+**Q-learning with epsilon-greedy policy with decaying epsilon at constant rate**
+![RL anonymity paradigm](images/q_learn_epsilon_greedy_decay_rate_avg_run_distortion.png "Reinforcement learning anonymity schematics")
+![RL anonymity paradigm](images/q_learn_epsilon_greedy_decay_rate_avg_run_reward.png "Reinforcement learning anonymity schematics")
+
+**Q-learning with softmax policy running average distorion**
+![RL anonymity paradigm](images/q_learn_softmax_avg_run_distortion.png "Reinforcement learning anonymity schematics")
+![RL anonymity paradigm](images/q_learn_softmax_avg_run_reward.png "Reinforcement learning anonymity schematics")
+
+
 ## Dependencies 
 
+- NumPy
+
 ## Documentation
 
+## References
+