Determines sets of 'near optimal' actions for all states

1 | ```
mdp_eval_policy_optimality(P, R, discount, Vpolicy)
``` |

`P` |
transition probability array. P can be a 3 dimensions array [S,S,A] or a list [[A]], each element containing a sparse matrix [S,S]. |

`R` |
reward array. R can be a 3 dimensions array [S,S,A] or a list [[A]], each element containing a sparse matrix [S,S] or a 2 dimensional matrix [S,A] possibly sparse. |

`discount` |
discount factor. discount is a real number which belongs to [0; 1[. |

`Vpolicy` |
value function of the optimal policy. Vpolicy is a S length vector. |

For some states, the evaluation of the value function may give close results for different actions. It is interesting to identify those states for which several actions have a value function very close the optimal one (i.e. less than 0.01 different). We called this the search for near optimal actions in each state.

`multiple` |
existence of at least two 'nearly' optimal actions for a state. multiple is egal to true when at least one state has several epsilon-optimal actions, false if not. |

`optimal_actions ` |
actions 'nearly' optimal for each state. optimal_actions is a [S,A] boolean matrix whose element optimal_actions(s, a) is true if the action a is 'nearly' optimal being in state s and false if not. |

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
# With a non-sparse matrix
P <- array(0, c(2,2,2))
P[,,1] <- matrix(c(0.5, 0.5, 0.8, 0.2), 2, 2, byrow=TRUE)
P[,,2] <- matrix(c(0, 1, 0.1, 0.9), 2, 2, byrow=TRUE)
R <- matrix(c(5, 10, -1, 2), 2, 2, byrow=TRUE)
Vpolicy <- c(42.4419, 36.0465)
mdp_eval_policy_optimality(P, R, 0.9, Vpolicy)
# With a sparse matrix
P <- list()
P[[1]] <- Matrix(c(0.5, 0.5, 0.8, 0.2), 2, 2, byrow=TRUE, sparse=TRUE)
P[[2]] <- Matrix(c(0, 1, 0.1, 0.9), 2, 2, byrow=TRUE, sparse=TRUE)
mdp_eval_policy_optimality(P, R, 0.9, Vpolicy)
``` |

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