AIToolbox
A library that offers tools for AI problem solving.
TopTwoThompsonSamplingPolicy.hpp
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1 #ifndef AI_TOOLBOX_BANDIT_TOP_TWO_THOMPSON_SAMPLING_POLICY_HEADER_FILE
2 #define AI_TOOLBOX_BANDIT_TOP_TWO_THOMPSON_SAMPLING_POLICY_HEADER_FILE
3 
8 
9 namespace AIToolbox::Bandit {
35  public:
42  TopTwoThompsonSamplingPolicy(const Experience & exp, double beta);
43 
49  virtual size_t sampleAction() const override;
50 
56  size_t recommendAction() const;
57 
71  virtual double getActionProbability(const size_t & a) const override;
72 
85  virtual Vector getPolicy() const override;
86 
92  const Experience & getExperience() const;
93 
94  private:
95  ThompsonSamplingPolicy policy_;
96  double beta_;
97  };
98 }
99 
100 #endif
AIToolbox::Bandit::TopTwoThompsonSamplingPolicy::getPolicy
virtual Vector getPolicy() const override
This function returns a vector containing all probabilities of the policy.
AIToolbox::Bandit::ThompsonSamplingPolicy
This class implements a Thompson sampling policy.
Definition: ThompsonSamplingPolicy.hpp:19
Experience.hpp
AIToolbox::Bandit::PolicyInterface
Simple typedef for most of a normal Bandit's policy needs.
Definition: PolicyInterface.hpp:11
AIToolbox::Bandit::TopTwoThompsonSamplingPolicy::getExperience
const Experience & getExperience() const
This function returns a reference to the underlying Experience we use.
AIToolbox::Vector
Eigen::Matrix< double, Eigen::Dynamic, 1 > Vector
Definition: Types.hpp:16
AIToolbox::Bandit::TopTwoThompsonSamplingPolicy
This class implements the top-two Thompson sampling policy.
Definition: TopTwoThompsonSamplingPolicy.hpp:34
AIToolbox::Bandit::TopTwoThompsonSamplingPolicy::TopTwoThompsonSamplingPolicy
TopTwoThompsonSamplingPolicy(const Experience &exp, double beta)
Basic constructor.
AIToolbox::Bandit
Definition: Experience.hpp:6
PolicyInterface.hpp
Types.hpp
AIToolbox::Bandit::Experience
This class computes averages and counts for a Bandit problem.
Definition: Experience.hpp:13
AIToolbox::Bandit::TopTwoThompsonSamplingPolicy::sampleAction
virtual size_t sampleAction() const override
This function chooses an action using top-two Thompson sampling.
AIToolbox::Bandit::TopTwoThompsonSamplingPolicy::recommendAction
size_t recommendAction() const
This function returns the most likely best action until this point.
AIToolbox::Bandit::TopTwoThompsonSamplingPolicy::getActionProbability
virtual double getActionProbability(const size_t &a) const override
This function returns the probability of taking the specified action.
ThompsonSamplingPolicy.hpp