diff --git a/perls2/envs/README.md b/perls2/envs/README.md
new file mode 100644
index 00000000..5ef17dd3
--- /dev/null
+++ b/perls2/envs/README.md
@@ -0,0 +1,63 @@
+# Env
+class Env(gym.Env): Abstract base class for environments.
+## Hierarchy
+Users define their own environment class by inheriting the abstract Environment (env.py). All perls2 environments use the OpenAI gym as a superclass. 
+**TODO** Image here
+Basic hierarchy for Env inheritance. 
+## Attributes:
+
+  -**config**: dict
+  
+Config files contain parameters to create an arena, robot interface, sensor interface and object interface. 
+They also contain specs for learning, simulation and experiment setup.
+ 
+  -**world**: World
+
+Object describing the simulation or reality in which contains an arena, robot interface, sensor interface and object interface.
+The world does not contain any information about observations, actions or task descriptions. 
+To streamline the setup of the experiment, worlds are typically generated from the configuration file using the `world_factory`.
+This ensures that the appropriate robot, sensor and object interfaces are quickly generated. The enclosing Env is given direct
+access to the world’s attributes. i.e.
+      
+	      env.robot_interface = env.world.robot_interface
+        
+ -**arena**: Arena
+        	Manages the sim by loading models (in both sim/real envs) and for
+        	simulations, randomizing objects and sensors parameters
+          
+-**robot_interface**: RobotInterface
+        	Communicates with robots and executes robot commands.
+          
+-**sensor_interface**: SensorInterface
+        	Retrieves sensor info and executes changes to params	
+ 
+-**object_interface**: ObjectInterface
+        	Only for Sim environments-- retrieves object info and excecutes
+        	changes to params
+
+## Public methods 
+(similar to openAI gym)
+
+-step:
+        	Step env forward and return observation, reward, termination, info.
+        	Not typically user-defined but may be modified.
+       	 
+-reset:
+        	Reset env to initial setting and return observation at initial state.
+        	Some aspects such as randomization are user-defined
+    	
+
+## Notes:
+Arenas and interfaces are, by default, initialized specific to the World of the env i.e. BulletWorld creates a BulletArena and Bullet_Robot/Sensor/Object_Interfaces.
+
+For any env, users need to set the following methods:
+        	
+-reset:
+Define aspects reset environment after episode completion. This may include randomization of objects or camera intrinsics or extrinsics
+-get_observation:
+          Define the observation returned by env upon reset, step
+-rewardFunction:
+         Compute the reward received by the agent given current state of
+         the environment
+-exec_action:
+          Define how action received by agent should be executed.