agent.greedy.Greedy Class Reference

An agent that always exploits, never explores. More...

Inheritance diagram for agent.greedy.Greedy:

Public Member Functions
None	__init__ (self, int k, float start_value=0.0)
	Construct the agent.
int	act (self)
	Select an action to take from the available ones.
None	update (self, int action, float reward)
	Update the table values based on the last action.
Public Member Functions inherited from agent.base_agent.BaseAgent
int	exploit (self)
	Select the best action.
int	explore (self)
	Explore a new action.
numpy.ndarray	table (self)
	Return the Q-Table.

Protected Attributes
	_n
Protected Attributes inherited from agent.base_agent.BaseAgent
	_table

Detailed Description

An agent that always exploits, never explores.

It will always pick the action with the highest value from the Q-table. While these values will be updated, it never explores, so will likely quickly converge on a single action.

Definition at line 4 of file greedy.py.

Constructor & Destructor Documentation

__init__()

None agent.greedy.Greedy.__init__	(		self,
		int	k,
		float	start_value = 0.0
	)

Construct the agent.

Parameters

k	The number of arms to select from. Should be an int greater than zero.
start_value	The starting reward to use for each arm. All arms assume the same value at the start.

Reimplemented from agent.base_agent.BaseAgent.

Definition at line 12 of file greedy.py.

Member Function Documentation

act()

int agent.greedy.Greedy.act

(

self

)

Select an action to take from the available ones.

Greedy always exploits, so this will always be one of the actions with the highest table value.

Returns

An int representing the selected action. It will be on the interval [0, k).

Reimplemented from agent.base_agent.BaseAgent.

Definition at line 23 of file greedy.py.

update()

None agent.greedy.Greedy.update	(		self,
		int	action,
		float	reward
	)

Update the table values based on the last action.

This uses an iterative version of a running average to update table values.

Parameters

action	The index corresponding to the action that was taken.
reward	The resulting reward that was earned.

Reimplemented from agent.base_agent.BaseAgent.

Definition at line 32 of file greedy.py.

Member Data Documentation

_n

agent.greedy.Greedy._n

protected

Definition at line 21 of file greedy.py.

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The documentation for this class was generated from the following file:

• agent/greedy.py