Specifications

abstract type Specification

Abstract type for problem specifications, which can define goal predicates, action costs, reward functions, and other desired criteria for planning Solutions.

is_goal(spec, domain, state, [action])

Check if state is a goal state according to the specification. If an action is provided, check if state is a goal state after executing action. For most specifications, action is ignored.

is_violated(spec, domain, state)

Check if state violates specified constraints.

get_cost(spec, domain, s1, a, s2)

Returns the cost of going from state s1 to state s2 via action a.

get_reward(spec, domain, s1, a, s2)

Returns the reward of going from state s1 to state s2 via action a.

get_discount(spec)

Returns the reward discount factor.

abstract type Goal <: Specification

Abstract type for goal-based specifications, which define a shortest path problem to a set of goal states. The set of goal states is typically defined by a list of terms that must hold true for the goal to be satisfied.

In the context of Markov decision processes, a goal state is a terminal state. If all actions also have positive cost (i.e. negative reward), this constitutes a stochastic shortest path problem.

get_goal_terms(spec)

Return goal terms.

set_goal_terms(spec, terms)

Return a copy of the goal specification with updated goal terms.

MinStepsGoal(terms)
MinStepsGoal(goal::Term)
MinStepsGoal(problem::Problem)

Goal specification where each step (i.e. action) has unit cost, and the goal formula is a conjunction of terms. Planners called with this specification will try to minimize the number of steps to the goal in the returned Solution.

MinMetricGoal(terms, metric::Term)
MinMetricGoal(goal::Term, metric::Term)
MinMetricGoal(problem::Problem)

Goal specification where each step has a cost specified by the difference in values of a metric formula between the next state and the current state, and the goal formula is a conjuction of terms.Planners called with this specification will try to minimize the metric formula when solving for the goal.

MaxMetricGoal(goals, metric::Term)
MaxMetricGoal(goal::Term, metric::Term)
MaxMetricGoal(problem::Problem)

Goal specification where each step has a reward specified by the difference in values of a metric formula between the next state and the current state, and the goal formula is a conjuction of terms.Planners called with this specification will try to maximize the metric formula when solving for the goal.

ActionGoal(action::Term, [constraints, step_cost])

Goal specification which requires that action is executed as the final step. Optionally, object constraints can be specified. These are either static constraints on the action's variable parameters, or predicates that must hold in the final state. The cost of each step in the solution is step_cost, which defaults to 1.0.

StateConstrainedGoal(goal::Goal, constraints::Vector{Term})

Goal specification with a list of constraints that must hold for every state. Planners that receive this specification are required to return plans or policies that ensure every visited state satisfies the constraints.

MinActionCosts(terms, costs)
MinActionCosts(terms, actions, costs)

Goal specification where each action has a specific cost, and the goal formula is a conjunction of terms. Planners called with this specification will try to minimize the total action cost in the returned Solution.

Costs can be provided as mapping from action names (specified as Symbols) to Reals, such that each lifted action has an associated cost. Alternatively, costs can be provided as a mapping from ground action Terms to Reals. A mapping can be provided directly as a NamedTuple or Dictionary, or as a list of actions and corresponding costs.

ExtraActionCosts(spec::Specification, costs)
ExtraActionCosts(spec::Specification, actions, costs)

Wrapper that adds action-specific costs to an underlying spec.

Costs can be provided as mapping from action names (specified as Symbols) to Reals, such that each lifted action has an associated cost. Alternatively, costs can be provided as a mapping from ground action Terms to Reals. A mapping can be provided directly as a NamedTuple or Dictionary, or as a list of actions and corresponding costs.

MinPerAgentActionCosts(terms, costs, [agent_arg_idx=1])

Goal specification where each agent has separate action costs. Planners called with this specification will try to minimize the total action cost in the returned Solution.

Costs can be provided as a nested dictionary or named tuple, where the first level maps agent names (specified as Symbols or Consts) to the second level, which maps action names or ground action Terms to Reals.

The agent_arg_idx argument specifies the index of the agent argument in the action terms. By default, this is 1.

ExtraPerAgentActionCosts(spec::Specification, costs, [agent_arg_idx=1])

Wrapper that adds per-agent action costs to an underlying spec.

Costs can be provided as a nested dictionary or named tuple, where the first level maps agent names (specified as Symbols or Consts) to the second level, which maps action names or ground action Terms to Reals.

has_action_cost(spec)

Returns whether a specification has action-specific costs.

get_action_cost(spec, action)

Returns the cost for act for specifications with fixed action costs.

infer_action_costs(domain, problem)

Infer fixed action costs for a domain and problem, returning nothing if unsuccessful.

infer_action_costs(
    domain::Domain, state::State, metric::Term,
    cost_fluents=PDDL.constituents(metric, domain),
    static_fluents=infer_static_fluents(domain)
)

Infer fixed action costs for a domain and initial state, and metric formula, returning nothing if unsuccessful.

GoalReward(terms, reward=1.0, discount=0.9)

Goal specification which returns a reward when all goal terms are achieved, along with a discount factor. Each action has zero cost.

BonusGoalReward(goal::Goal, reward=1.0, discount=0.9)

Wrapper around an existing Goal specification, which delivers additional reward upon reaching a goal.

MultiGoalReward(goals::Vector{Term}, rewards::Vector{Float64}, discount=1.0)

Goal specification where multiple goals have associated rewards. Achieving a goal delivers the associated reward. Each action has zero cost.

DiscountedReward(spec::Specification, discount::Float64)

Discounts rewards of the underlying spec by a discount factor.

discounted(spec, discount)

Discount the rewards or costs associated with spec by a discount factor.