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from libcpp.vector cimport vector |
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cdef class MinMaxStatsList: |
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cdef CMinMaxStatsList *cmin_max_stats_lst |
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def __cinit__(self, int num): |
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self.cmin_max_stats_lst = new CMinMaxStatsList(num) |
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def set_delta(self, float value_delta_max): |
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self.cmin_max_stats_lst[0].set_delta(value_delta_max) |
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def __dealloc__(self): |
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del self.cmin_max_stats_lst |
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cdef class ResultsWrapper: |
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cdef CSearchResults cresults |
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def __cinit__(self, int num): |
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self.cresults = CSearchResults(num) |
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def get_search_len(self): |
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return self.cresults.search_lens |
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cdef class Action: |
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cdef int is_root_action |
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cdef vector[float] value |
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cdef CAction action |
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def __cinit__(self): |
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pass |
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def __cinit__(self, vector[float] value, int is_root_action): |
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self.is_root_action = is_root_action |
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self.value = value |
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cdef class Roots: |
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cdef int root_num |
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cdef int action_space_size |
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cdef int num_of_sampled_actions |
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cdef CRoots *roots |
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cdef bool continuous_action_space |
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def __cinit__(self): |
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pass |
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def __cinit__(self, int root_num, list legal_actions_list, int action_space_size, int num_of_sampled_actions, |
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bool continuous_action_space): |
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self.root_num = root_num |
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self.action_space_size = action_space_size |
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self.num_of_sampled_actions = num_of_sampled_actions |
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self.roots = new CRoots(root_num, legal_actions_list, action_space_size, num_of_sampled_actions, |
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continuous_action_space) |
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def prepare(self, float root_noise_weight, list noises, list value_prefix_pool, list policy_logits_pool, |
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vector[int] & to_play_batch): |
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self.roots[0].prepare(root_noise_weight, noises, value_prefix_pool, policy_logits_pool, to_play_batch) |
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def prepare_no_noise(self, list value_prefix_pool, list policy_logits_pool, vector[int] & to_play_batch): |
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self.roots[0].prepare_no_noise(value_prefix_pool, policy_logits_pool, to_play_batch) |
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def get_trajectories(self): |
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return self.roots[0].get_trajectories() |
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def get_distributions(self): |
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return self.roots[0].get_distributions() |
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def get_sampled_actions(self): |
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return self.roots[0].get_sampled_actions() |
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def get_values(self): |
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return self.roots[0].get_values() |
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def clear(self): |
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self.roots[0].clear() |
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def __dealloc__(self): |
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del self.roots |
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@property |
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def num(self): |
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return self.root_num |
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cdef class Node: |
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cdef CNode cnode |
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cdef bool continuous_action_space |
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def __cinit__(self): |
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pass |
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def __cinit__(self, float prior, vector[int] & legal_actions, int action_space_size, int num_of_sampled_actions, |
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bool continuous_action_space): |
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pass |
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def expand(self, int to_play, int current_latent_state_index, int batch_index, float value_prefix, |
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list policy_logits): |
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cdef vector[float] cpolicy = policy_logits |
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self.cnode.expand(to_play, current_latent_state_index, batch_index, value_prefix, cpolicy) |
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def batch_backpropagate(int current_latent_state_index, float discount_factor, list value_prefixs, list values, list policies, |
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MinMaxStatsList min_max_stats_lst, ResultsWrapper results, list is_reset_list, |
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list to_play_batch): |
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cdef int i |
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cdef vector[float] cvalue_prefixs = value_prefixs |
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cdef vector[float] cvalues = values |
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cdef vector[vector[float]] cpolicies = policies |
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cbatch_backpropagate(current_latent_state_index, discount_factor, cvalue_prefixs, cvalues, cpolicies, |
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min_max_stats_lst.cmin_max_stats_lst, results.cresults, is_reset_list, to_play_batch) |
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def batch_traverse(Roots roots, int pb_c_base, float pb_c_init, float discount_factor, MinMaxStatsList min_max_stats_lst, |
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ResultsWrapper results, list virtual_to_play_batch, bool continuous_action_space): |
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cbatch_traverse(roots.roots, pb_c_base, pb_c_init, discount_factor, min_max_stats_lst.cmin_max_stats_lst, results.cresults, |
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virtual_to_play_batch, continuous_action_space) |
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return results.cresults.latent_state_index_in_search_path, results.cresults.latent_state_index_in_batch, results.cresults.last_actions, results.cresults.virtual_to_play_batchs |
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