diff --git a/.gitignore b/.gitignore
index 0e73de6..dbe20d5 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,5 +1,6 @@
 # IPython Notebook Checkpoints
 **/.ipynb_checkpoints
+**/__pycache__
 
 # Documentation builds
 **/_build
\ No newline at end of file
diff --git a/README.md b/README.md
index 11b1240..86cbf7a 100644
--- a/README.md
+++ b/README.md
@@ -13,7 +13,7 @@ Agentpy is a package for the development and analysis of agent-based models in P
 
 **Documentation:** https://agentpy.readthedocs.io
 
-**Tutorials:** https://agentpy.readthedocs.io/tutorials.html
+**Tutorials:** https://agentpy.readthedocs.io/en/latest/tutorials.html
 
 **Requirements:** Python 3, NumPy, scipy, matplotlib, networkx, pandas, SALib, and ipywidgets.
 
diff --git a/agentpy/experiment.py b/agentpy/experiment.py
index b750eea..5cf8e16 100644
--- a/agentpy/experiment.py
+++ b/agentpy/experiment.py
@@ -24,16 +24,19 @@
 
 class experiment():
     
-    """ Experiment for an agent-based model. Can also be called as exp().
+    """ Experiment for an agent-based model.
+    Allows for multiple iterations, parameter samples, and distict scenarios.
     
     Arguments:
         model(class): The model that the experiment should use.
         parameters(dict or list): Parameters or parameter sample.
         name(str,optional): Name of the experiment. Takes model name at default.
         scenarios(str or list,optional): Scenarios that should be tested, if any.
-        iterations(int,optional): How often to repeat the experiment. Default 1.
+        iterations(int,optional): How often to repeat the experiment (default 1).
         output_vars(bool,optional): Whether to record dynamic variables. Default False.
-        display(bool,optional): Whether to display simulation progress. Default False.
+        
+    Attributes:
+        output(data_dict): Recorded experiment data
     
     """
     
@@ -67,8 +70,16 @@ def __init__( self,
     
     def run(self, display=True):
              
-        """ Performs the experiment and generates `output`. """
+        """ Executes the simulation of the experiment. 
+        It will run the model once for each set of parameters, 
+        and will repeat this process for each iteration.
         
+        Arguments:
+            display(bool,optional): Whether to display simulation progress (default True). 
+            
+        Returns:
+            data_dict: Recorded experiment data, also stored in `experiment.output`.
+        """
         parameter_sample = make_list(self.parameters,keep_none=True)
         scenarios = make_list(self.scenarios,keep_none=True)
         runs = parameter_sample * self.iterations
@@ -183,13 +194,11 @@ def sample(param_ranges,mode='discrete',**kwargs):
     """
     Returns parameter sample (list of dict)
     
-    Arguments
-    ---------
-    param_ranges: dict
-    mode: str
-        'discrete' - tuples are given of style (value1,value2,...)
-        'saltelli' - tuples are given of style (min_value,max_value)
-
+    Arguments:
+        param_ranges(dict)
+        mode(str): Sampling method. Options are:
+            'discrete' - tuples are given of style (value1,value2,...); 
+            'saltelli' - tuples are given of style (min_value,max_value)
     """
     
     if mode == 'discrete': parameters = create_sample_discrete(param_ranges,**kwargs)
diff --git a/agentpy/framework.py b/agentpy/framework.py
index d86c5d5..fef976f 100644
--- a/agentpy/framework.py
+++ b/agentpy/framework.py
@@ -32,8 +32,9 @@ def record(self, var_keys, value = None):
     
     for var_key in make_list(var_keys):
     
-        # Create empty list if var_key is new (!) should have none until t
-        if var_key not in self.log: self.log[var_key] = [None] * len(self.log['t'])
+        # Create empty list if var_key is new 
+        if var_key not in self.log: 
+            self.log[var_key] = [None] * len(self.log['t'])
 
         if self.model.t not in self.log['t']:
 
@@ -59,9 +60,7 @@ def setup(self):
 
 class agent(attr_dict):   
     
-    """ 
-    
-    Individual agent of an agent-based model.
+    """ Individual agent of an agent-based model.
     This class can be used as a parent class for custom agent types.  
     Attributes can be accessed as items (see :class:`attr_dict`).
     Methods of :class:`agent_list` can also be used for individual agents.
@@ -127,9 +126,9 @@ def neighbors(self,key=None):
         elif key not in self.envs.keys(): AgentpyError(f"Agent {self.id} has no network '{key}'")
         if key == None: AgentpyError(f"No network found for agent {self.id}")
         
-        return agent_list([n for n in self.envs[key].neighbors(self)]) # (!) list comprehension necessary?
+        return agent_list([n for n in self.envs[key].neighbors(self)]) 
     
-    def remove(self,keys=None):
+    def leave(self,keys=None):
         
         """ Removes the agent from environments or the model 
         
@@ -142,7 +141,7 @@ def remove(self,keys=None):
             envs = self.envs # Select all environments by default
             self.model.agents.remove(self) # Delete from model    
         else: 
-            envs = { k:v for k,v in self.envs if k in keys}
+            envs = { k:v for k,v in self.envs.items() if k in keys}
             
         for key,env in envs.items(): 
             env.agents.remove(self)
@@ -237,30 +236,30 @@ def add_agents(self, agents, agent_class=agent, **kwargs):
         agents(int or agent_list): Number of new agents or list of existing agents.
         agent_class(class): Type of agent to be created if int is passed for agents.
     """ 
-    
-    # (!) what if single agent is passed? 
-
-    if type(agents) == agent_list: # (!) or normal iterable with agents?
-
-        new_agents = agents
-        # (!) need to add new environment to agent
         
-    elif type(agents) == int:
+    if isinstance(agents,int):
         
-        new_agents = agent_list()
+        # Create new agent instances
+        new_agents = agent_list([agent_class(self.model, {self.key:self}, **kwargs) for _ in range(agents)])   
         
-        for i in range(agents): 
-            new_agents.append(agent_class(self.model, {self.key:self}, **kwargs))
-                 
-    else: raise ValueError("agents must be agent_list or int") # (!) improve error
+        # Add agents to master list
+        if self != self.model:    
+            self.model.agents.extend( new_agents )
+    
+    else:
+        
+        # Add existing agents
+        if isinstance(agents,agent_list): new_agents = agents
+        else: new_agents = agent_list(agents)
+        
+        # Add new environment to agents
+        if self != self.model: 
+            for agent in new_agents:
+                agent.envs[self.key] = self
         
     # Add agents to this env
     self.agents.extend( new_agents )
 
-    # Add agent to master list
-    if self != self.model: 
-            self.model.agents.extend( new_agents )
-
     return new_agents
     
 def _env_init(self,model,key):
@@ -340,26 +339,24 @@ def add_agents(self, agents, agent_class=agent, map_to_nodes=False, **kwargs):
         Adds agents to the network environment.
 
         Arguments:
-            agents(int or agent_list): Number of new agents or list of existing agents.
+            agents(int or agent or list or agent_list): Number of new agents or existing agents.
             agent_class(class): Type of agent to be created if int is passed for agents.
             map_to_nodes(bool,optional): Map new agents to each node of the graph (default False).
                 Should be used if a graph with empty nodes has been passed at network creation.
         """         
-
+        
+        # Standard adding
         new_agents = add_agents(self,agents,agent_class,**kwargs)
         
-        if map_to_nodes:
-            
+        # Extra network features
+        if map_to_nodes: 
             # Map each agent to a node of the graph
-            
             if len(new_agents) != len(self.graph.nodes):
                 raise ValueError(f"Number of agents ({len(new_agents)}) does not match number of nodes ({len(self.graph.nodes)})")
-                
             mapping = { i : agent for i,agent in enumerate(new_agents) }
             nx.relabel_nodes( self.graph , mapping = mapping, copy=False )
-            
         else:
-            # Add agents to graph
+            # Add agents to graph as new nodes
             for agent in new_agents:
                 self.graph.add_node( agent ) 
         
@@ -383,35 +380,32 @@ class env_dict(dict):
     """
 
     def __init__(self,model):
+        
         super().__init__()
         self.model = model    
             
     def of_type(self,env_type):
         
-        """ Returns an env_dict with environments of `env_type`"""
+        """ Returns an env_dict with selected environments """
         
         new_dict = env_dict(self.model)
         selection = {k : v for k, v in self.items() if v.type == env_type}
         new_dict.update(selection)
         return new_dict
     
-    def do(self,action):
+    def do(self,method,*args,**kwargs):
         
-        """ Calls `action` for all environments """
+        """ Calls method for all environments """
         
-        for env in self.values(): getattr( env, action )() 
+        for env in self.values(): getattr(env, method)(*args, **kwargs) 
     
     def add_agents(self,*args,**kwargs):
         
-        """ Adds agents to all environments in self """
-        
-        # Depreciated (!)
+        """ Adds agents to all environments """
         
-        new_agents = agent_list(self.model)
-        new_agents.add(*args,**kwargs)
-        
-        for env in self.values():
-            env.agents.add(new_agents)
+        for i,env in enumerate(self.values()):
+            if i == 0: new_agents = env.add_agents(*args,**kwargs)
+            else: env.add_agents( new_agents )
 
 
 ### Level 1 - Model Class ###
@@ -492,20 +486,18 @@ def __getattr__(self, name):
 
     def add_env(self, env_key, env_class=environment, **kwargs):
 
-        """ Creates and returns a new environment """
-
-        self.envs[env_key] = env_class(self.model, env_key, **kwargs) 
-        return self.envs[env_key] 
-    
-    def add_envs(self,env_keys,*args,**kwargs):
-        for env_key in env_keys: self.add_env(env_key,*args,**kwargs)
+        """ Creates a new environment """
+        
+        for env_key in make_list(env_key):
+            self.envs[env_key] = env_class(self.model, env_key, **kwargs) 
                     
     def add_network(self, env_key, graph = None, env_class=network , **kwargs):
-        self.add_env(env_key, env_class=network, graph=graph, **kwargs)
-            
-    def add_networks(self,env_keys,*args,**kwargs):      
-        for env_key in env_keys: self.add_network(env_key,*args,**kwargs)
-            
+        
+        """ Creates a new network environment """
+        
+        for env_key in make_list(env_key):
+            self.add_env(env_key, env_class=network, graph=graph, **kwargs)
+
     
     # Recording functions
             
@@ -514,27 +506,10 @@ def measure(self, measure_key, value):
         """ Records an evaluation measure """
         
         self.measure_log[measure_key] = [value]
-        
-    def record_graph(self, graph_keys):  
-        
-        """ Records a network """ # (!) unfinished
-        
-        for graph_key in make_list(graph_keys):
-            
-            G = self.envs[graph_key].graph
-            H = nx.relabel_nodes(G, lambda x: x.id)
-            # (!) graph attributes? H.graph.t = self.t
-            if 'graphs' not in self.model.output.keys():
-                self.model.output['graphs'] = [] #{'t':[]}
-            self.model.output['graphs'].append(H)
-            #if graph_key not in self.model.output['graphs'].keys():
-            #    self.model.output['graphs'][graph_key] = []
-            #self.model.output['graphs'][graph_key].append(H)
-            #self.model.output['graphs']['t'].append(self.t)
     
     def record_all(self):
         
-        """ Records all model variables """
+        """ Records all dynamic model variables """
         
         keys = list(self.keys())
         for key in self._int_keys:
@@ -545,63 +520,57 @@ def record_all(self):
     # Main simulation functions 
     
     def step(self):
-        """ 
-        Defines the model's actions during each simulation step.
-        Can be overwritten and used to perform the models' main dynamics.
-        """
+        """ Defines the model's actions during each simulation step.
+        Can be overwritten and used to perform the models' main dynamics."""
         pass
     
     def update(self):
-        """ 
-        Defines the model's actions after setup and each simulation step.
-        Can be overwritten and used for the recording of dynamic variables.
-        """
+        """ Defines the model's actions after setup and each simulation step.
+        Can be overwritten and used for the recording of dynamic variables. """
         pass  
     
     def end(self):
-        """ 
-        Defines the model's actions after the last simulation step.
-        Can be overwritten and used for final calculations and the recording of evaluation measures.
-        """
+        """ Defines the model's actions after the last simulation step.
+        Can be overwritten and used to calculate evaluation measures."""
         pass
     
     def stop_if(self):
         """ 
-        Stops the simulation if return value is `False`. 
-        Returns whether time-step 't' has reached the parameter 'steps'.
+        Stops :meth:`model.run` during an active simulation if it returns `False`. 
         Can be overwritten with a custom function.
+        
+        Returns:
+            bool: Whether time-step `t` has reached the parameter `model.p.steps`.
         """
         return self.t >= self.p.steps 
     
     def stop(self):
-        """ Stops the simulation. """
-        self.stopped = True       
+        """ Stops :meth:`model.run` during an active simulation. """
+        self._stop = True       
             
     def run(self,display=True):
         
         """ Executes the simulation of the model.
-        
-        The order of events is as follows:
-        
-        - setup()
-        - update()
-        - while stop_if() returns False and stop() hasn't been called:
-            - t += 1
-            - step()
-            - update()
-        - end()
+        The order of events is as follows.
+        The simulation starts at `t=0` and calls setup() and update().
+        While stop_if() returns False and stop() hasn't been called,
+        the simulation repeatedly calls t+=1, step(), and update().
+        After the last step, end() is called.
         
         Arguments:
             display(bool,optional): Whether to display simulation progress (default True).
+            
+        Returns:
+            data_dict: Recorded model data, also stored in `model.output`.
         """
         
         t0 = datetime.now() # Time-Stamp
         
-        self.stopped = False
+        self._stop = False
         self.setup() 
         self.update()
         
-        while not self.stop_if() and not self.stopped:
+        while not self.stop_if() and not self._stop:
             
             self.t += 1
             self.step()
@@ -609,7 +578,6 @@ def run(self,display=True):
             
             if display: print( f"\rCompleted: {self.t} steps" , end='' ) 
         
-        self.stopped = True
         self.end() 
         self.create_output()
         self.output.log['run_time'] = ct = str( datetime.now() - t0 )
diff --git a/agentpy/interactive.py b/agentpy/interactive.py
index ab5dfca..6f0c23e 100644
--- a/agentpy/interactive.py
+++ b/agentpy/interactive.py
@@ -17,7 +17,7 @@ def animate(model, parameters, fig, axs, plot, skip_t0 = False, **kwargs):
     """ Returns an animation of the model simulation """
     
     m = model(parameters)
-    m.stopped = False
+    m._stop = False
     m.setup()
     m.update()
 
@@ -28,7 +28,7 @@ def frames():
 
         nonlocal m, step0, step00
 
-        while not m.stop_if() and not m.stopped:
+        while not m.stop_if() and not m._stop:
 
             if step0: step0 = False
             elif step00: step00 = False
diff --git a/agentpy/output.py b/agentpy/output.py
index dfe4151..a8f695a 100644
--- a/agentpy/output.py
+++ b/agentpy/output.py
@@ -16,10 +16,7 @@
 
 class data_dict(attr_dict):
     
-    """
-    Agentpy dictionary for output data
-    
-    """
+    """ Dictionary for recorded data from :class:`model` and :class:`experiment` simulations. """
     
     def __init__(self):
         super().__init__() 
@@ -46,6 +43,7 @@ def __repr__(self):
                 
         return rep + "\n}"
     
+    
     def ps_keys(self):
         
         """ Returns the keys of varied parameters """
@@ -76,6 +74,7 @@ def get_pars(self, parameters = None):
             
         return dfp  
     
+    
     def get_measures(self,measures=None,parameters=None,reset_index=True):
         
         """ Returns pandas dataframe with measures """
@@ -90,6 +89,7 @@ def get_measures(self,measures=None,parameters=None,reset_index=True):
             
         return df 
     
+    
     def get_vars(self,var_keys=None,obj_types=None,parameters=None,reset_index=True):
 
         """ Returns pandas dataframe with variables """
diff --git a/agentpy/tools.py b/agentpy/tools.py
index a59d28a..4d3277d 100644
--- a/agentpy/tools.py
+++ b/agentpy/tools.py
@@ -10,6 +10,8 @@
 class AgentpyError(Exception):
     pass
 
+
+
 class attr_dict(dict):
     
     """ Dictionary where attributes and dict entries are identical. """
@@ -50,8 +52,6 @@ def __call__(self,*args,**kwargs):
         try: return [ func_obj(*args,**kwargs) for func_obj in self ]
         except TypeError: raise TypeError(f"Not all objects in '{type(self._super).__name__}' are callable.")
     
-    # Operators, see https://docs.python.org/3/library/operator.html
-    
     def __eq__(self, other):
         
         return type(self._super)([obj for obj,x in zip(self._super,self) if x == other])
diff --git a/docs/T01_Wealth_Transfer.ipynb b/docs/T01_Wealth_Transfer.ipynb
index 7a4350f..1e0f0a2 100644
--- a/docs/T01_Wealth_Transfer.ipynb
+++ b/docs/T01_Wealth_Transfer.ipynb
@@ -91,7 +91,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "All agents are contained within an object of the class `model`. The model initializes with the above defined agents, the number of which will be taken from the models' parameters. In each time-step, `wealth_transfer `is activated for all agents in a random order, after which each agents `wealth` and a model variable `Gini Coefficient` are recorded."
+    "All agents are contained within an object of the class `model`. The model initializes with the above defined agents, the number of which will be taken from the models' parameters. In each time-step, `wealth_transfer` is activated for all agents in a random order, after which a model variable `Gini Coefficient` are recorded. At the end of the simulation, each agents `wealth` is also recorded."
    ]
   },
   {
@@ -136,7 +136,7 @@
    "source": [
     "parameters = {\n",
     "    'agents': 100,\n",
-    "    'steps': 100,\n",
+    "    'steps': 100\n",
     "}"
    ]
   },
diff --git a/docs/T02_Virus_Spread.ipynb b/docs/T02_Virus_Spread.ipynb
index 81f8a64..516ca10 100644
--- a/docs/T02_Virus_Spread.ipynb
+++ b/docs/T02_Virus_Spread.ipynb
@@ -51164,7 +51164,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The function `sobol_sensitivity()` calculates sobol sensitivity indices for the passed results and parameter ranges, using the [SAlib]( https://salib.readthedocs.io/en/latest/basics.html) package. This adds two new categories to our results:\n",
+    "The function `sensitivity()` calculates sobol sensitivity indices for the passed results and parameter ranges, using the [SAlib]( https://salib.readthedocs.io/en/latest/basics.html) package. This adds two new categories to our results:\n",
     "\n",
     "- `sensitivity` - first-order sobol sensitivity indices\n",
     "- `sensitivity_conf` - confidence ranges for the above indices"
@@ -51280,7 +51280,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We can use the pandas functionalities to create a bar plot `plot_sensitivity()` that visualizes our sensitivities."
+    "We can use the pandas functionalities to create a bar plot `plot_sobol_indices()` that visualizes our sensitivities."
    ]
   },
   {
diff --git a/docs/conf.py b/docs/conf.py
index f5e8cc2..ff56fc5 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -51,7 +51,7 @@
 nbsphinx_prolog = """
 .. currentmodule:: agentpy
 .. note::
-	You can download this tutorial as a Jupyter Notebook :download:`here<{{ env.doc2path(env.docname,base=None) }}>` (Save link to file)
+	You can download this tutorial as a Jupyter Notebook :download:`here<{{ env.doc2path(env.docname,base=None) }}>`
 """
 
 # Intersphinx mapping
diff --git a/docs/installation.rst b/docs/installation.rst
index 19ff87a..e20ea3b 100644
--- a/docs/installation.rst
+++ b/docs/installation.rst
@@ -12,7 +12,7 @@ To install the package, run the following command on your console::
 
 Alternatively, you can also clone the latest version from GitHub::
 
-	$ git clone hhttps://github.com/JoelForamitti/agentpy.git
+	$ git clone https://github.com/JoelForamitti/agentpy.git
 
 The agentpy packet can then be imported as follows (recommended)::
 
diff --git a/docs/reference.rst b/docs/reference.rst
index b054ece..ab9b4be 100644
--- a/docs/reference.rst
+++ b/docs/reference.rst
@@ -60,8 +60,8 @@ Parameter sampling
 
 .. autofunction:: sample
 
-Multi-run experiments
----------------------
+Experiment class
+----------------
 
 .. autoclass:: experiment
    :members: