Metadata-Version: 2.1
Name: learnrl
Version: 0.1.0
Summary: A package to learn about Reinforcement Learning
Home-page: https://github.com/MathisFederico/LearnRL
Author: Mathïs Fédérico
Author-email: mathfederico@gmail.com
License: UNKNOWN
Description: # LearnRL
        A library to use and learn reinforcement learning algorithms
        
        ## General framework for reinforcement learning agents
        
        Every agents can be implemented as a subclass of this general agent !
        QLearning, DeepQLearning, A3C based agents ... and this librairie tries to prove it !
        
        ### Memory
        
        All the agents share the same memory system optimized with numpy to speedup computation
        See agents.agent for more details
        
        ### Table Agent (With any controls or evaluations) including the famous QLearning !
        
        Table agents builds a table of actions values Q(state, action)
        From this table the agent uses a Control algorithm to decide what action to play in each state:
            Control: state(s) -> action to play
        
        Throught experience it updates this table using an evaluation algorithm:
            Evaluation: experience -> Q update
        
        The QLearningAgent is already pre-built and ready for use ! Just import it from agents !
        
        See agents.basic.agent for more details
        
        #### Control
        
        Commons controls are built-in : Greedy, UpperConfidenceBound(UCB), PolynomialUpperConfidenceTrees(Puct)
        But you can build any control algorithm by extanding the class Control !
        You just have to code the function policy:
            policy(state(s), action_values(Q), action_visits(N)) -> probability of each action
        
        See agents.basic.control for more details
        
        #### Evaluation
        
        Commons evaluations are built-in : MonteCarlo, TemporalDifference(lambda)
        But you can build any control algorithm by extanding the class Evaluation !
        You just have to code the function learn:
            learn(action_values(Q), action_visits(N), memory:Memory) -> update Q and N
        
        See agents.basic.evaluation for more details
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU Lesser General Public License v3 or later (LGPLv3+)
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
