Lex-MAB

Implementations of PF-LEX, OM-LEX, and NOM-LEX for the multi-armed bandit with lexicographically ordered objectives (Lex-MAB).

In all implementations:

• A is the number of arms,
• D is the number of objectives,
• means is an AxD matrix, where means[a,i] is the expected reward of arm a in objective i, and arm 0 is assumed to be lexicographic optimal,
• K is the number of individual runs,
• T is the number of rounds,
• reg is a DxKxT matrix, where reg[i,k,t] is the regret incurred in objective i, individual run k, and round t.

In the implementation of PF-LEX:

• dlt is the confidence term,
• eps is proportional to the suboptimality that the learner aims to tolerate,
• TT is the period in which the regrets are recorded. Note that this script takes an argument called uid, which is a unique identifier with which the final results are saved. This is to allow for parallel execution of the script.

In the implementation of NOM-LEX:

• eta is a 1xD matrix, where eta[0,i] is the near lexicographic optimal expected reward in objective i.

Sat-MAB

In addition to algorithms for Lex-MAB, 'satisficing.py' includes an adapted version of NOM-LEX for the multi-armed bandit with satisficing objectives (Sat-MAB), along with an implementation of Satisficing-In-Mean-Rewards UCL (Reverdy et al., "Satisficing in multi-armed bandit problems," 2017).