R: Solution Methods for Multi-Objective Markov Decision Processes

# Solution Methods for Multi-Objective Markov Decision Processes

## Documentation for package ‘multiobjectiveMDP’ version 1.0.0

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS

## Help Pages

are_valid_finite_horizon_rewards | Determine whether a numeric list represents a valid reward structure for finite-horizon problems
are_valid_finite_horizon_transition_probabilities | Determine whether a numeric list represents a valid transition probability structure for finite-horizon problems
are_valid_infinite_horizon_rewards | Determine whether a numeric list represents a valid reward structure for infinite-horizon problems
are_valid_infinite_horizon_transition_probabilities | Determine whether a numeric list represents a valid transition probability structure for infinite-horizon problems
compromise_solution | Calculate the compromise solution among a set of objective vectors
discounted_bellman_operator | Apply a stationary Bellman-type operator to a vector-valued value function
efficient_subset_sort_prune | Find the Pareto efficient subset of a set of vectors
evaluate_discounted_MMDP_pure_policy | Evaluate a stationary policy in a discounted infinite-horizon multi-objective Markov decision process
evaluate_finite_horizon_MMDP_markov_policy | Evaluate a Markov deterministic policy for a finite-horizon multi-objective Markov decision process
generate_rand_MMDP | Generate a random instance of a multi-objective Markov decision process
is_valid_finite_horizon_policy | Determine whether an integer matrix represents a policy for a given class of finite-horizon problems
is_valid_infinite_horizon_policy | Determine whether an integer vector represents a stationary policy for a given class of infinite-horizon problems
solve_discounted_MDP_policy_iteration | Optimize a discounted infinite-horizon Markov decision process through policy iteration
solve_discounted_MMDP_linear_programming | Optimize a discounted infinite-horizon multi-objective Markov decision process through linear programming
solve_discounted_MMDP_policy_iteration | Optimize a discounted infinite-horizon multi-objective Markov decision process through policy iteration
solve_discounted_MMDP_weighting_factor | Optimize a discounted infinite-horizon multi-objective Markov decision process through the weighting factor approach
solve_finite_horizon_MDP_backward_induction | Solve a standard finite-horizon Markov decision process through dynamic programming
solve_finite_horizon_MMDP_backward_induction | Optimize a finite-horizon multi-objective Markov decision process through vector-valued dynamic programming
solve_finite_horizon_MMDP_linear_programming | Optimize a finite-horizon multi-objective Markov decision process through linear programming
solve_finite_horizon_MMDP_weighting_factor | Optimize a finite-horizon multi-objective Markov decision process through the weighting factor approach
solve_MOLP | Solve a multi-objective linear programming problem by a simplex-type method
sum_set | Calculate the sum set (Minkowski sum) of two or more sets of vectors
value_function_domination_sets | Compare two or more vector-valued value functions