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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. |
Contents
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
Richard Valenzano, Nathan R. Sturtevant, Jonathan Schaeffer and Fan Xie.
A Comparison of Knowledge-Based GBFS Enhancements and Knowledge-Free Exploration.
In Proceedings of the Twenty-Fourth International Conference on Automated Planning and Scheduling (ICAPS 2014), pp. 375-379. AAAI Press 2014.
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
Fan Xie, Martin Mueller, Robert Holte and Tatsuya Imai.
Type-Based Exploration with Multiple Search Queues for Satisficing Planning.
In Proceedings of the Twenty-Eigth AAAI Conference Conference on Artificial Intelligence (AAAI 2014), pp. 2395-2401. AAAI Press 2014.
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.