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ReinforcementLearning

Model-Free Reinforcement Learning

v1.0.5 · Mar 1, 2020 · MIT + file LICENSE
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Description

Performs model-free reinforcement learning in R. This implementation enables the learning of an optimal policy based on sample sequences consisting of states, actions and rewards. In addition, it supplies multiple predefined reinforcement learning algorithms, such as experience replay. Methodological details can be found in Sutton and Barto (1998) <ISBN:0262039249>.

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dependencies in R code

              
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                12 OK ·                 2 NOTE ·                 0 WARNING ·                 0 ERROR ·                 0 FAILURE              
              Mar 9, 2026
            
            

              

                

                  NOTE
                  r-devel-linux-x86_64-fedora-clang
                

                
dependencies in R code

                
Namespace in Imports field not imported from: ‘ggplot2’
  All declared Imports should be used.

              

              

                

                  NOTE
                  r-devel-linux-x86_64-fedora-gcc
                

                
dependencies in R code

                
Namespace in Imports field not imported from: ‘ggplot2’
  All declared Imports should be used.

              

            

          

        

      


      
      

        
Reverse Dependencies (1)

        

          
imports

          

            lazytrade
          

        

      


      
      

        
Dependency Network

        

          
            

            
            
            Dependencies
            
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              ggplot2
            
            
              
              hash
            
            
              
              data.table
            

            
            
              
              lazytrade
            

            
            
            ReinforcementLearning
          
        

      


      
      

        
Version History

        

          

            new
            1.0.5
            Mar 9, 2026