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epsilon_greedy(eval, pref_only=false, epsilon=0.2) epsilon_greedy(eval, pref_only=false, epsilon=0.2, random_seed=-1)
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 * ''random_seed'' (int [-1, infinity]): Set to -1 (default) to use the global random number generator. Set to any other value to use a local random number generator with the given seed.
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pareto(evals, pref_only=false, state_uniform_selection=false) pareto(evals, pref_only=false, state_uniform_selection=false, random_seed=-1)
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 * ''random_seed'' (int [-1, infinity]): Set to -1 (default) to use the global random number generator. Set to any other value to use a local random number generator with the given seed.
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type_based(evaluators) type_based(evaluators, random_seed=-1)
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 * ''random_seed'' (int [-1, infinity]): Set to -1 (default) to use the global random number generator. Set to any other value to use a local random number generator with the given seed.

Alternation open list

alternates between several open lists.

alt(sublists, boost=0)
  • sublists (list of OpenList): open lists between which this one alternates

  • boost (int): boost value for contained open lists that are restricted to preferred successors

Epsilon-greedy open list

Chooses an entry uniformly randomly with probability 'epsilon', otherwise it returns the minimum entry. The algorithm is based on

epsilon_greedy(eval, pref_only=false, epsilon=0.2, random_seed=-1)
  • eval (ScalarEvaluator): scalar evaluator

  • pref_only (bool): insert only nodes generated by preferred operators

  • epsilon (double [0.0, 1.0]): probability for choosing the next entry randomly

  • random_seed (int [-1, infinity]): Set to -1 (default) to use the global random number generator. Set to any other value to use a local random number generator with the given seed.

Pareto open list

Selects one of the Pareto-optimal (regarding the sub-evaluators) entries for removal.

pareto(evals, pref_only=false, state_uniform_selection=false, random_seed=-1)
  • evals (list of ScalarEvaluator): scalar evaluators

  • pref_only (bool): insert only nodes generated by preferred operators

  • state_uniform_selection (bool): When removing an entry, we select a non-dominated bucket and return its oldest entry. If this option is false, we select uniformly from the non-dominated buckets; if the option is true, we weight the buckets with the number of entries.

  • random_seed (int [-1, infinity]): Set to -1 (default) to use the global random number generator. Set to any other value to use a local random number generator with the given seed.

Standard open list

Standard open list that uses a single evaluator

single(eval, pref_only=false)
  • eval (ScalarEvaluator): scalar evaluator

  • pref_only (bool): insert only nodes generated by preferred operators

Bucket-based open list

Bucket-based open list implementation that uses a single evaluator. Ties are broken in FIFO order.

single_buckets(eval, pref_only=false)
  • eval (ScalarEvaluator): scalar evaluator

  • pref_only (bool): insert only nodes generated by preferred operators

Tie-breaking open list

tiebreaking(evals, pref_only=false, unsafe_pruning=true)
  • evals (list of ScalarEvaluator): scalar evaluators

  • pref_only (bool): insert only nodes generated by preferred operators

  • unsafe_pruning (bool): allow unsafe pruning when the main evaluator regards a state a dead end

Type-based open list

Uses multiple evaluators to assign entries to buckets. All entries in a bucket have the same evaluator values. When retrieving an entry, a bucket is chosen uniformly at random and one of the contained entries is selected uniformly randomly. The algorithm is based on

type_based(evaluators, random_seed=-1)
  • evaluators (list of ScalarEvaluator): Evaluators used to determine the bucket for each entry.

  • random_seed (int [-1, infinity]): Set to -1 (default) to use the global random number generator. Set to any other value to use a local random number generator with the given seed.

FastDownward: Doc/OpenList (last edited 2019-03-08 12:08:33 by XmlRpcBot)