Update README.md

README.md

 ### GOAL simulator + Policy generator ### 
 #### STEPS:
-###### - 1: Create initial user cognitive model from data (human therapist and patient)
-###### - 2: Create robot policy from data (human therapist) or update it if session > 0
-###### - 3: Run the simulation using the [BN_GenerativeModel](https://github.com/aandriella/BN_GenerativeModel) package
-###### - 4: Generate new episodes
-###### - 5: Learn the robot reward using Max Causal Entropy Inverse Reiforcement Learning algorithm proposed Ziebart's thesis (2010) [MaxEntropyIRL](https://github.com/aandriella/MaxEntRL)
-###### - 6: Compute the policy related to that reward using Value Iteration
-###### - 7: Run a session between the robot and the patient
-###### - Repeat from 2
+ -1. Create initial user cognitive model from data (human therapist and patient) <br />
+ -2. Create robot policy from data (human therapist) or update it if session > 0  <br />
+ -3. Run the simulation using the [BN_GenerativeModel](https://github.com/aandriella/BN_GenerativeModel) package  <br />
+ -4. Generate new episodes  <br />
+ -5. Learn the robot reward using [Maximum Causal Entropy Inverse Reiforcement Learning](https://github.com/aandriella/MaxEntIRL) algorithm proposed Ziebart's thesis (2010)   <br />
+ -6. Compute the policy related to that reward using Value Iteration  <br />
+ -7. Run a session between the robot and the patient  <br />
+ -Repeat from 2  <br />
 
 
 #### Package:
@@ -23,15 +23,15 @@
 python main.py --bn_model_folder ($PATH) --output_policy_filename ($PATH) --output_reward_filename ($PATH) --output_value_filename ($PATH) --therapist_patient_interaction_folder ($PATH) --agent_patient_interaction_folder ($PATH) --epoch 5 --run 50 --user_id 19 --f True --s 0 --objective neutral
 ```
 where:
-- bn_model_folder, folder contains the initial bn models of therapist and patient
-- epoch, the number of epoch of the simulation
-- run, number of runs per epoch 
-- output_policy_filename, output of the computed policy
-- output_reward_filename, output of the computed reward
-- output_value_filename, output of the computed value function
-- therapist_patient_interaction_folder, data containing the logs from the  sessions between the human therapist and the patient
-- agent_patient_interaction_folder, data containing the logs from the  sessions between the robot therapist and the patient
-- user_id, id of the user 
-- with_feedback, if  [SOCIABLE](http://www.iri.upc.edu/files/scidoc/2353-Discovering-SOCIABLE:-Using-a-conceptual-model-to-evaluate-the-legibility-and-effectiveness-of-backchannel-cues-in-an-entertainment-scenario.pdf)  is used 
-- session,  the session id
-- agent_objective, objective can be either challenge if we want to challenge more the user, help if we want to help more the user or finally it can neutral so we do not reshape the policy.
+-- bn_model_folder, folder contains the initial bn models of therapist and patient <br />
+-- epoch, the number of epoch of the simulation  <br />
+-- run, number of runs per epoch  <br />
+-- output_policy_filename, output of the computed policy  <br />
+-- output_reward_filename, output of the computed reward  <br />
+-- output_value_filename, output of the computed value function  <br />
+-- therapist_patient_interaction_folder, data containing the logs from the  sessions between the human therapist and the patient  <br />
+-- agent_patient_interaction_folder, data containing the logs from the  sessions between the robot therapist and the patient  <br />
+-- user_id, id of the user   <br />
+-- with_feedback, if  [SOCIABLE](http://www.iri.upc.edu/files/scidoc/2353-Discovering-SOCIABLE:-Using-a-conceptual-model-to-evaluate-the-legibility-and-effectiveness-of-backchannel-cues-in-an-entertainment-scenario.pdf)  is used   <br />
+-- session,  the session id  <br />
+-- agent_objective, objective can be either challenge if we want to challenge more the user, help if we want to help more the user or finally it can neutral so we do not reshape the policy.