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// C++11
#include <iostream>
#include "cnode.h"
#include <algorithm>
#include <map>
#include <cmath>
#include <random>
#include <numeric>
#ifdef _WIN32
#include "..\..\common_lib\utils.cpp"
#else
#include "../../common_lib/utils.cpp"
#endif
namespace tree{
CSearchResults::CSearchResults()
{
/*
Overview:
Initialization of CSearchResults, the default result number is set to 0.
*/
this->num = 0;
}
CSearchResults::CSearchResults(int num)
{
/*
Overview:
Initialization of CSearchResults with result number.
*/
this->num = num;
for (int i = 0; i < num; ++i)
{
this->search_paths.push_back(std::vector<CNode *>());
}
}
CSearchResults::~CSearchResults(){}
//*********************************************************
CNode::CNode()
{
/*
Overview:
Initialization of CNode.
*/
this->prior = 0;
this->legal_actions = legal_actions;
this->visit_count = 0;
this->value_sum = 0;
this->raw_value = 0; // the value network approximation of value
this->best_action = -1;
this->to_play = 0;
this->reward = 0.0;
// gumbel muzero related code
this->gumbel_scale = 10.0;
this->gumbel_rng=0.0;
}
CNode::CNode(float prior, std::vector<int> &legal_actions)
{
/*
Overview:
Initialization of CNode with prior value and legal actions.
Arguments:
- prior: the prior value of this node.
- legal_actions: a vector of legal actions of this node.
*/
this->prior = prior;
this->legal_actions = legal_actions;
this->visit_count = 0;
this->value_sum = 0;
this->raw_value = 0; // the value network approximation of value
this->best_action = -1;
this->to_play = 0;
this->current_latent_state_index = -1;
this->batch_index = -1;
// gumbel muzero related code
this->gumbel_scale = 10.0;
this->gumbel_rng=0.0;
this->gumbel = generate_gumbel(this->gumbel_scale, this->gumbel_rng, legal_actions.size());
}
CNode::~CNode(){}
void CNode::expand(int to_play, int current_latent_state_index, int batch_index, float reward, float value, const std::vector<float> &policy_logits)
{
/*
Overview:
Expand the child nodes of the current node.
Arguments:
- to_play: which player to play the game in the current node.
- current_latent_state_index: The index of latent state of the leaf node in the search path of the current node.
- batch_index: The index of latent state of the leaf node in the search path of the current node.
- reward: the reward of the current node.
- value: the value network approximation of current node.
- policy_logits: the logit of the child nodes.
*/
this->to_play = to_play;
this->current_latent_state_index = current_latent_state_index;
this->batch_index = batch_index;
this->reward = reward;
this->raw_value = value;
int action_num = policy_logits.size();
if (this->legal_actions.size() == 0)
{
for (int i = 0; i < action_num; ++i)
{
this->legal_actions.push_back(i);
}
}
float temp_policy;
float policy_sum = 0.0;
#ifdef _WIN32
// 创建动态数组
float* policy = new float[action_num];
#else
float policy[action_num];
#endif
float policy_max = FLOAT_MIN;
for(auto a: this->legal_actions){
if(policy_max < policy_logits[a]){
policy_max = policy_logits[a];
}
}
for(auto a: this->legal_actions){
temp_policy = exp(policy_logits[a] - policy_max);
policy_sum += temp_policy;
policy[a] = temp_policy;
}
float prior;
for(auto a: this->legal_actions){
prior = policy[a] / policy_sum;
std::vector<int> tmp_empty;
this->children[a] = CNode(prior, tmp_empty); // only for muzero/efficient zero, not support alphazero
}
#ifdef _WIN32
// 释放数组内存
delete[] policy;
#else
#endif
}
void CNode::add_exploration_noise(float exploration_fraction, const std::vector<float> &noises)
{
/*
Overview:
Add a noise to the prior of the child nodes.
Arguments:
- exploration_fraction: the fraction to add noise.
- noises: the vector of noises added to each child node.
*/
float noise, prior;
for(int i =0; i<this->legal_actions.size(); ++i){
noise = noises[i];
CNode* child = this->get_child(this->legal_actions[i]);
prior = child->prior;
child->prior = prior * (1 - exploration_fraction) + noise * exploration_fraction;
}
}
//*********************************************************
// Gumbel Muzero related code
//*********************************************************
std::vector<float> CNode::get_q(float discount_factor)
{
/*
Overview:
Compute the q value of the current node.
Arguments:
- discount_factor: the discount_factor of reward.
*/
std::vector<float> child_value;
for(auto a: this->legal_actions){
CNode* child = this->get_child(a);
float true_reward = child->reward;
float qsa = true_reward + discount_factor * child->value();
child_value.push_back(qsa);
}
return child_value;
}
float CNode::compute_mean_q(int isRoot, float parent_q, float discount_factor)
{
/*
Overview:
Compute the mean q value of the current node.
Arguments:
- isRoot: whether the current node is a root node.
- parent_q: the q value of the parent node.
- discount_factor: the discount_factor of reward.
*/
float total_unsigned_q = 0.0;
int total_visits = 0;
for(auto a: this->legal_actions){
CNode* child = this->get_child(a);
if(child->visit_count > 0){
float true_reward = child->reward;
float qsa = true_reward + discount_factor * child->value();
total_unsigned_q += qsa;
total_visits += 1;
}
}
float mean_q = 0.0;
if(isRoot && total_visits > 0){
mean_q = (total_unsigned_q) / (total_visits);
}
else{
mean_q = (parent_q + total_unsigned_q) / (total_visits + 1);
}
return mean_q;
}
void CNode::print_out()
{
return;
}
int CNode::expanded()
{
/*
Overview:
Return whether the current node is expanded.
*/
return this->children.size() > 0;
}
float CNode::value()
{
/*
Overview:
Return the real value of the current tree.
*/
float true_value = 0.0;
if (this->visit_count == 0)
{
return true_value;
}
else
{
true_value = this->value_sum / this->visit_count;
return true_value;
}
}
std::vector<int> CNode::get_trajectory()
{
/*
Overview:
Find the current best trajectory starts from the current node.
Outputs:
- traj: a vector of node index, which is the current best trajectory from this node.
*/
std::vector<int> traj;
CNode *node = this;
int best_action = node->best_action;
while (best_action >= 0)
{
traj.push_back(best_action);
node = node->get_child(best_action);
best_action = node->best_action;
}
return traj;
}
std::vector<int> CNode::get_children_distribution()
{
/*
Overview:
Get the distribution of child nodes in the format of visit_count.
Outputs:
- distribution: a vector of distribution of child nodes in the format of visit count (i.e. [1,3,0,2,5]).
*/
std::vector<int> distribution;
if (this->expanded())
{
for (auto a : this->legal_actions)
{
CNode *child = this->get_child(a);
distribution.push_back(child->visit_count);
}
}
return distribution;
}
//*********************************************************
// Gumbel Muzero related code
//*********************************************************
std::vector<float> CNode::get_children_value(float discount_factor, int action_space_size)
{
/*
Overview:
Get the completed value of child nodes.
Outputs:
- discount_factor: the discount_factor of reward.
- action_space_size: the size of action space.
*/
float infymin = -std::numeric_limits<float>::infinity();
std::vector<int> child_visit_count;
std::vector<float> child_prior;
for(auto a: this->legal_actions){
CNode* child = this->get_child(a);
child_visit_count.push_back(child->visit_count);
child_prior.push_back(child->prior);
}
assert(child_visit_count.size()==child_prior.size());
// compute the completed value
std::vector<float> completed_qvalues = qtransform_completed_by_mix_value(this, child_visit_count, child_prior, discount_factor);
std::vector<float> values;
for (int i=0;i<action_space_size;i++){
values.push_back(infymin);
}
for (int i=0;i<child_prior.size();i++){
values[this->legal_actions[i]] = completed_qvalues[i];
}
return values;
}
CNode *CNode::get_child(int action)
{
/*
Overview:
Get the child node corresponding to the input action.
Arguments:
- action: the action to get child.
*/
return &(this->children[action]);
}
//*********************************************************
// Gumbel Muzero related code
//*********************************************************
std::vector<float> CNode::get_policy(float discount_factor, int action_space_size){
/*
Overview:
Compute the improved policy of the current node.
Arguments:
- discount_factor: the discount_factor of reward.
- action_space_size: the action space size of environment.
*/
float infymin = -std::numeric_limits<float>::infinity();
std::vector<int> child_visit_count;
std::vector<float> child_prior;
for(auto a: this->legal_actions){
CNode* child = this->get_child(a);
child_visit_count.push_back(child->visit_count);
child_prior.push_back(child->prior);
}
assert(child_visit_count.size()==child_prior.size());
// compute the completed value
std::vector<float> completed_qvalues = qtransform_completed_by_mix_value(this, child_visit_count, child_prior, discount_factor);
std::vector<float> probs;
for (int i=0;i<action_space_size;i++){
probs.push_back(infymin);
}
for (int i=0;i<child_prior.size();i++){
probs[this->legal_actions[i]] = child_prior[i] + completed_qvalues[i];
}
csoftmax(probs, probs.size());
return probs;
}
//*********************************************************
CRoots::CRoots()
{
/*
Overview:
The initialization of CRoots.
*/
this->root_num = 0;
}
CRoots::CRoots(int root_num, std::vector<std::vector<int> > &legal_actions_list)
{
/*
Overview:
The initialization of CRoots with root num and legal action lists.
Arguments:
- root_num: the number of the current root.
- legal_action_list: the vector of the legal action of this root.
*/
this->root_num = root_num;
this->legal_actions_list = legal_actions_list;
for (int i = 0; i < root_num; ++i)
{
this->roots.push_back(CNode(0, this->legal_actions_list[i]));
}
}
CRoots::~CRoots() {}
void CRoots::prepare(float root_noise_weight, const std::vector<std::vector<float> > &noises, const std::vector<float> &rewards, const std::vector<float> &values, const std::vector<std::vector<float> > &policies, std::vector<int> &to_play_batch)
{
/*
Overview:
Expand the roots and add noises.
Arguments:
- root_noise_weight: the exploration fraction of roots.
- noises: the vector of noise add to the roots.
- rewards: the vector of rewards of each root.
- values: the vector of values of each root.
- policies: the vector of policy logits of each root.
- to_play_batch: the vector of the player side of each root.
*/
for(int i = 0; i < this->root_num; ++i){
this->roots[i].expand(to_play_batch[i], 0, i, rewards[i], values[i], policies[i]);
this->roots[i].add_exploration_noise(root_noise_weight, noises[i]);
this->roots[i].visit_count += 1;
}
}
void CRoots::prepare_no_noise(const std::vector<float> &rewards, const std::vector<float> &values, const std::vector<std::vector<float> > &policies, std::vector<int> &to_play_batch)
{
/*
Overview:
Expand the roots without noise.
Arguments:
- rewards: the vector of rewards of each root.
- values: the vector of values of each root.
- policies: the vector of policy logits of each root.
- to_play_batch: the vector of the player side of each root.
*/
for(int i = 0; i < this->root_num; ++i){
this->roots[i].expand(to_play_batch[i], 0, i, rewards[i], values[i], policies[i]);
this->roots[i].visit_count += 1;
}
}
void CRoots::clear()
{
/*
Overview:
Clear the roots vector.
*/
this->roots.clear();
}
std::vector<std::vector<int> > CRoots::get_trajectories()
{
/*
Overview:
Find the current best trajectory starts from each root.
Outputs:
- traj: a vector of node index, which is the current best trajectory from each root.
*/
std::vector<std::vector<int> > trajs;
trajs.reserve(this->root_num);
for (int i = 0; i < this->root_num; ++i)
{
trajs.push_back(this->roots[i].get_trajectory());
}
return trajs;
}
std::vector<std::vector<int> > CRoots::get_distributions()
{
/*
Overview:
Get the children distribution of each root.
Outputs:
- distribution: a vector of distribution of child nodes in the format of visit count (i.e. [1,3,0,2,5]).
*/
std::vector<std::vector<int> > distributions;
distributions.reserve(this->root_num);
for (int i = 0; i < this->root_num; ++i)
{
distributions.push_back(this->roots[i].get_children_distribution());
}
return distributions;
}
//*********************************************************
// Gumbel Muzero related code
//*********************************************************
std::vector<std::vector<float> > CRoots::get_children_values(float discount_factor, int action_space_size)
{
/*
Overview:
Compute the completed value of each root.
Arguments:
- discount_factor: the discount_factor of reward.
- action_space_size: the action space size of environment.
*/
std::vector<std::vector<float> > values;
values.reserve(this->root_num);
for (int i = 0; i < this->root_num; ++i)
{
values.push_back(this->roots[i].get_children_value(discount_factor, action_space_size));
}
return values;
}
std::vector<std::vector<float> > CRoots::get_policies(float discount_factor, int action_space_size)
{
/*
Overview:
Compute the improved policy of each root.
Arguments:
- discount_factor: the discount_factor of reward.
- action_space_size: the action space size of environment.
*/
std::vector<std::vector<float> > probs;
probs.reserve(this->root_num);
for(int i = 0; i < this->root_num; ++i){
probs.push_back(this->roots[i].get_policy(discount_factor, action_space_size));
}
return probs;
}
std::vector<float> CRoots::get_values()
{
/*
Overview:
Return the real value of each root.
*/
std::vector<float> values;
for (int i = 0; i < this->root_num; ++i)
{
values.push_back(this->roots[i].value());
}
return values;
}
//*********************************************************
//
void update_tree_q(CNode* root, tools::CMinMaxStats &min_max_stats, float discount_factor, int players)
{
/*
Overview:
Update the q value of the root and its child nodes.
Arguments:
- root: the root that update q value from.
- min_max_stats: a tool used to min-max normalize the q value.
- discount_factor: the discount factor of reward.
- players: the number of players.
*/
std::stack<CNode*> node_stack;
node_stack.push(root);
// float parent_value_prefix = 0.0;
while(node_stack.size() > 0){
CNode* node = node_stack.top();
node_stack.pop();
if(node != root){
// # NOTE: in 2 player mode, value_prefix is not calculated according to the perspective of current player of node,
// # but treated as 1 player, just for obtaining the true reward in the perspective of current player of node.
// # true_reward = node.value_prefix - (- parent_value_prefix)
// float true_reward = node->value_prefix - node->parent_value_prefix;
float true_reward = node->reward;
float qsa;
if(players == 1)
qsa = true_reward + discount_factor * node->value();
else if(players == 2)
// TODO(pu):
qsa = true_reward + discount_factor * (-1) * node->value();
min_max_stats.update(qsa);
}
for(auto a: node->legal_actions){
CNode* child = node->get_child(a);
if(child->expanded()){
// child->parent_value_prefix = node->value_prefix;
node_stack.push(child);
}
}
}
}
void cback_propagate(std::vector<CNode*> &search_path, tools::CMinMaxStats &min_max_stats, int to_play, float value, float discount_factor)
{
/*
Overview:
Update the value sum and visit count of nodes along the search path.
Arguments:
- search_path: a vector of nodes on the search path.
- min_max_stats: a tool used to min-max normalize the q value.
- to_play: which player to play the game in the current node.
- value: the value to propagate along the search path.
- discount_factor: the discount factor of reward.
*/
assert(to_play == -1);
float bootstrap_value = value;
int path_len = search_path.size();
for(int i = path_len - 1; i >= 0; --i){
CNode* node = search_path[i];
node->value_sum += bootstrap_value;
node->visit_count += 1;
float true_reward = node->reward;
min_max_stats.update(true_reward + discount_factor * node->value());
bootstrap_value = true_reward + discount_factor * bootstrap_value;
}
}
void cbatch_back_propagate(int current_latent_state_index, float discount_factor, const std::vector<float> &value_prefixs, const std::vector<float> &values, const std::vector<std::vector<float> > &policies, tools::CMinMaxStatsList *min_max_stats_lst, CSearchResults &results, std::vector<int> &to_play_batch)
{
/*
Overview:
Expand the nodes along the search path and update the infos.
Arguments:
- current_latent_state_index: The index of latent state of the leaf node in the search path.
- discount_factor: the discount factor of reward.
- value_prefixs: the value prefixs of nodes along the search path.
- values: the values to propagate along the search path.
- policies: the policy logits of nodes along the search path.
- min_max_stats: a tool used to min-max normalize the q value.
- results: the search results.
- to_play_batch: the batch of which player is playing on this node.
*/
for(int i = 0; i < results.num; ++i){
results.nodes[i]->expand(to_play_batch[i], current_latent_state_index, i, value_prefixs[i], values[i], policies[i]);
cback_propagate(results.search_paths[i], min_max_stats_lst->stats_lst[i], to_play_batch[i], values[i], discount_factor);
}
}
int cselect_child(CNode* root, tools::CMinMaxStats &min_max_stats, int pb_c_base, float pb_c_init, float discount_factor, float mean_q, int players)
{
/*
Overview:
Select the child node of the roots according to ucb scores.
Arguments:
- root: the roots to select the child node.
- min_max_stats: a tool used to min-max normalize the score.
- pb_c_base: constants c2 in muzero.
- pb_c_init: constants c1 in muzero.
- disount_factor: the discount factor of reward.
- mean_q: the mean q value of the parent node.
- players: the number of players.
Outputs:
- action: the action to select.
*/
float max_score = FLOAT_MIN;
const float epsilon = 0.000001;
std::vector<int> max_index_lst;
for(auto a: root->legal_actions){
CNode* child = root->get_child(a);
float temp_score = cucb_score(child, min_max_stats, mean_q, root->visit_count - 1, pb_c_base, pb_c_init, discount_factor, players);
if(max_score < temp_score){
max_score = temp_score;
max_index_lst.clear();
max_index_lst.push_back(a);
}
else if(temp_score >= max_score - epsilon){
max_index_lst.push_back(a);
}
}
int action = 0;
if(max_index_lst.size() > 0){
int rand_index = rand() % max_index_lst.size();
action = max_index_lst[rand_index];
}
return action;
}
//*********************************************************
// Gumbel Muzero related code
//*********************************************************
int cselect_root_child(CNode* root, float discount_factor, int num_simulations, int max_num_considered_actions)
{
/*
Overview:
Select the child node of the roots in gumbel muzero.
Arguments:
- root: the roots to select the child node.
- disount_factor: the discount factor of reward.
- num_simulations: the upper limit number of simulations.
- max_num_considered_actions: the maximum number of considered actions.
Outputs:
- action: the action to select.
*/
std::vector<int> child_visit_count;
std::vector<float> child_prior;
for(auto a: root->legal_actions){
CNode* child = root->get_child(a);
child_visit_count.push_back(child->visit_count);
child_prior.push_back(child->prior);
}
assert(child_visit_count.size()==child_prior.size());
std::vector<float> completed_qvalues = qtransform_completed_by_mix_value(root, child_visit_count, child_prior, discount_factor);
std::vector<std::vector<int> > visit_table = get_table_of_considered_visits(max_num_considered_actions, num_simulations);
int num_valid_actions = root->legal_actions.size();
int num_considered = std::min(max_num_considered_actions, num_simulations);
int simulation_index = std::accumulate(child_visit_count.begin(), child_visit_count.end(), 0);
int considered_visit = visit_table[num_considered][simulation_index];
std::vector<float> score = score_considered(considered_visit, root->gumbel, child_prior, completed_qvalues, child_visit_count);
float argmax = -std::numeric_limits<float>::infinity();
int max_action = root->legal_actions[0];
int index = 0;
for(auto a: root->legal_actions){
if(score[index] > argmax){
argmax = score[index];
max_action = a;
}
index += 1;
}
return max_action;
}
int cselect_interior_child(CNode* root, float discount_factor)
{
/*
Overview:
Select the child node of the interior node in gumbel muzero.
Arguments:
- root: the roots to select the child node.
- disount_factor: the discount factor of reward.
Outputs:
- action: the action to select.
*/
std::vector<int> child_visit_count;
std::vector<float> child_prior;
for(auto a: root->legal_actions){
CNode* child = root->get_child(a);
child_visit_count.push_back(child->visit_count);
child_prior.push_back(child->prior);
}
assert(child_visit_count.size()==child_prior.size());
std::vector<float> completed_qvalues = qtransform_completed_by_mix_value(root, child_visit_count, child_prior, discount_factor);
std::vector<float> probs;
for (int i=0;i<child_prior.size();i++){
probs.push_back(child_prior[i] + completed_qvalues[i]);
}
csoftmax(probs, probs.size());
int visit_count_sum = std::accumulate(child_visit_count.begin(), child_visit_count.end(), 0);
std::vector<float> to_argmax;
for (int i=0;i<probs.size();i++){
to_argmax.push_back(probs[i] - (float)child_visit_count[i]/(float)(1+visit_count_sum));
}
float argmax = -std::numeric_limits<float>::infinity();
int max_action = root->legal_actions[0];
int index = 0;
for(auto a: root->legal_actions){
if(to_argmax[index] > argmax){
argmax = to_argmax[index];
max_action = a;
}
index += 1;
}
return max_action;
}
float cucb_score(CNode *child, tools::CMinMaxStats &min_max_stats, float parent_mean_q, float total_children_visit_counts, float pb_c_base, float pb_c_init, float discount_factor, int players)
{
/*
Overview:
Compute the ucb score of the child.
Arguments:
- child: the child node to compute ucb score.
- min_max_stats: a tool used to min-max normalize the score.
- mean_q: the mean q value of the parent node.
- total_children_visit_counts: the total visit counts of the child nodes of the parent node.
- pb_c_base: constants c2 in muzero.
- pb_c_init: constants c1 in muzero.
- disount_factor: the discount factor of reward.
- players: the number of players.
Outputs:
- ucb_value: the ucb score of the child.
*/
float pb_c = 0.0, prior_score = 0.0, value_score = 0.0;
pb_c = log((total_children_visit_counts + pb_c_base + 1) / pb_c_base) + pb_c_init;
pb_c *= (sqrt(total_children_visit_counts) / (child->visit_count + 1));
prior_score = pb_c * child->prior;
if (child->visit_count == 0){
value_score = parent_mean_q;
}
else {
float true_reward = child->reward;
if(players == 1)
value_score = true_reward + discount_factor * child->value();
else if(players == 2)
value_score = true_reward + discount_factor * (-child->value());
}
value_score = min_max_stats.normalize(value_score);
if (value_score < 0) value_score = 0;
if (value_score > 1) value_score = 1;
float ucb_value = prior_score + value_score;
return ucb_value;
}
void cbatch_traverse(CRoots *roots, int num_simulations, int max_num_considered_actions, float discount_factor, CSearchResults &results, std::vector<int> &virtual_to_play_batch)
{
/*
Overview:
Search node path from the roots.
Arguments:
- roots: the roots that search from.
- num_simulations: the upper limit number of simulations.
- max_num_considered_actions: the maximum number of considered actions.
- disount_factor: the discount factor of reward.
- results: the search results.
- virtual_to_play_batch: the batch of which player is playing on this node.
*/
// set seed
timeval t1;
gettimeofday(&t1, NULL);
srand(t1.tv_usec);
int last_action = -1;
float parent_q = 0.0;
results.search_lens = std::vector<int>();
int players = 0;
int largest_element = *max_element(virtual_to_play_batch.begin(),virtual_to_play_batch.end()); // 0 or 2
if(largest_element==-1)
players = 1;
else
players = 2;
for(int i = 0; i < results.num; ++i){
CNode *node = &(roots->roots[i]);
int is_root = 1;
int search_len = 0;
int action = 0;
results.search_paths[i].push_back(node);
while(node->expanded()){
if(is_root){
action = cselect_root_child(node, discount_factor, num_simulations, max_num_considered_actions);
}
else{
action = cselect_interior_child(node, discount_factor);
}
is_root = 0;
node->best_action = action;
// next
node = node->get_child(action);
last_action = action;
results.search_paths[i].push_back(node);
search_len += 1;
}
CNode* parent = results.search_paths[i][results.search_paths[i].size() - 2];
results.latent_state_index_in_search_path.push_back(parent->current_latent_state_index);
results.latent_state_index_in_batch.push_back(parent->batch_index);
results.last_actions.push_back(last_action);
results.search_lens.push_back(search_len);
results.nodes.push_back(node);
results.virtual_to_play_batchs.push_back(virtual_to_play_batch[i]);
}
}
//*********************************************************
// Gumbel Muzero related code
//*********************************************************
void csoftmax(std::vector<float> &input, int input_len)
{
/*
Overview:
Softmax transformation.
Arguments:
- input: the vector to be transformed.
- input_len: the length of input vector.
*/
assert (input != NULL);
assert (input_len != 0);
int i;
float m;
// Find maximum value from input array
m = input[0];
for (i = 1; i < input_len; i++) {
if (input[i] > m) {
m = input[i];
}
}
float sum = 0;
for (i = 0; i < input_len; i++) {
sum += expf(input[i]-m);
}
for (i = 0; i < input_len; i++) {
input[i] = expf(input[i] - m - log(sum));
}
}
float compute_mixed_value(float raw_value, std::vector<float> q_values, std::vector<int> &child_visit, std::vector<float> &child_prior)
{
/*
Overview:
Compute the mixed Q value.
Arguments:
- raw_value: the approximated value of the current node from the value network.
- q_value: the q value of the current node.
- child_visit: the visit counts of the child nodes.
- child_prior: the prior of the child nodes.
Outputs:
- mixed Q value.
*/
float visit_count_sum = 0.0;
float probs_sum = 0.0;
float weighted_q_sum = 0.0;
float min_num = -10e7;
for(unsigned int i = 0;i < child_visit.size();i++)
visit_count_sum += child_visit[i];
for(unsigned int i = 0;i < child_prior.size();i++)
// Ensuring non-nan prior
child_prior[i] = std::max(child_prior[i], min_num);
for(unsigned int i = 0;i < child_prior.size();i++)
if (child_visit[i] > 0)
probs_sum += child_prior[i];
for (unsigned int i = 0;i < child_prior.size();i++)
if (child_visit[i] > 0){
weighted_q_sum += child_prior[i] * q_values[i] / probs_sum;
}
return (raw_value + visit_count_sum * weighted_q_sum) / (visit_count_sum+1);
}
void rescale_qvalues(std::vector<float> &value, float epsilon){
/*
Overview:
Rescale the q value with max-min normalization.
Arguments:
- value: the value vector to be rescaled.
- epsilon: the lower limit of gap.
*/
float max_value = *max_element(value.begin(), value.end());
float min_value = *min_element(value.begin(), value.end());
float gap = max_value - min_value;
gap = std::max(gap, epsilon);
for (unsigned int i = 0;i < value.size();i++){
value[i] = (value[i]-min_value)/gap;
}
}
std::vector<float> qtransform_completed_by_mix_value(CNode *root, std::vector<int> & child_visit, \
std::vector<float> & child_prior, float discount_factor, float maxvisit_init, float value_scale, \
bool rescale_values, float epsilon)
{
/*
Overview:
Calculate the q value with mixed value.
Arguments:
- root: the roots that search from.
- child_visit: the visit counts of the child nodes.
- child_prior: the prior of the child nodes.
- discount_factor: the discount factor of reward.
- maxvisit_init: the init of the maximization of visit counts.
- value_cale: the scale of value.
- rescale_values: whether to rescale the values.
- epsilon: the lower limit of gap in max-min normalization
Outputs:
- completed Q value.
*/
assert (child_visit.size() == child_prior.size());
std::vector<float> qvalues;
std::vector<float> child_prior_tmp;
child_prior_tmp.assign(child_prior.begin(), child_prior.end());
qvalues = root->get_q(discount_factor);
csoftmax(child_prior_tmp, child_prior_tmp.size());
// TODO: should be raw_value here
float value = compute_mixed_value(root->raw_value, qvalues, child_visit, child_prior_tmp);
std::vector<float> completed_qvalue;
for (unsigned int i = 0;i < child_prior_tmp.size();i++){
if (child_visit[i] > 0){
completed_qvalue.push_back(qvalues[i]);
}
else{
completed_qvalue.push_back(value);
}
}
if (rescale_values){
rescale_qvalues(completed_qvalue, epsilon);
}
float max_visit = *max_element(child_visit.begin(), child_visit.end());
float visit_scale = maxvisit_init + max_visit;
for (unsigned int i=0;i < completed_qvalue.size();i++){
completed_qvalue[i] = completed_qvalue[i] * visit_scale * value_scale;
}
return completed_qvalue;
}
std::vector<int> get_sequence_of_considered_visits(int max_num_considered_actions, int num_simulations)
{
/*
Overview:
Calculate the considered visit sequence.
Arguments:
- max_num_considered_actions: the maximum number of considered actions.
- num_simulations: the upper limit number of simulations.
Outputs:
- the considered visit sequence.
*/
std::vector<int> visit_seq;
if(max_num_considered_actions <= 1){
for (int i=0;i < num_simulations;i++)
visit_seq.push_back(i);
return visit_seq;
}
int log2max = std::ceil(std::log2(max_num_considered_actions));
std::vector<int> visits;
for (int i = 0;i < max_num_considered_actions;i++)
visits.push_back(0);
int num_considered = max_num_considered_actions;
while (visit_seq.size() < num_simulations){
int num_extra_visits = std::max(1, (int)(num_simulations / (log2max * num_considered)));
for (int i = 0;i < num_extra_visits;i++){
visit_seq.insert(visit_seq.end(), visits.begin(), visits.begin() + num_considered);
for (int j = 0;j < num_considered;j++)
visits[j] += 1;
}
num_considered = std::max(2, num_considered/2);
}
std::vector<int> visit_seq_slice;
visit_seq_slice.assign(visit_seq.begin(), visit_seq.begin() + num_simulations);
return visit_seq_slice;
}
std::vector<std::vector<int> > get_table_of_considered_visits(int max_num_considered_actions, int num_simulations)
{
/*
Overview:
Calculate the table of considered visits.
Arguments:
- max_num_considered_actions: the maximum number of considered actions.
- num_simulations: the upper limit number of simulations.
Outputs:
- the table of considered visits.
*/
std::vector<std::vector<int> > table;
for (int m=0;m < max_num_considered_actions+1;m++){
table.push_back(get_sequence_of_considered_visits(m, num_simulations));
}
return table;
}
std::vector<float> score_considered(int considered_visit, std::vector<float> gumbel, std::vector<float> logits, std::vector<float> normalized_qvalues, std::vector<int> visit_counts)
{
/*
Overview:
Calculate the score of nodes to be considered according to the considered visit.
Arguments:
- considered_visit: the visit counts of node to be considered.
- gumbel: the gumbel vector.
- logits: the logits vector of child nodes.
- normalized_qvalues: the normalized Q values of child nodes.
- visit_counts: the visit counts of child nodes.
Outputs:
- the score of nodes to be considered.
*/
float low_logit = -1e9;
float max_logit = *max_element(logits.begin(), logits.end());
for (unsigned int i=0;i < logits.size();i++){
logits[i] -= max_logit;
}
std::vector<float> penalty;
for (unsigned int i=0;i < visit_counts.size();i++){
// Only consider the nodes with specific visit counts
if (visit_counts[i]==considered_visit)
penalty.push_back(0);
else
penalty.push_back(-std::numeric_limits<float>::infinity());
}
assert(gumbel.size()==logits.size()==normalized_qvalues.size()==penalty.size());
std::vector<float> score;
for (unsigned int i=0;i < visit_counts.size();i++){
score.push_back(std::max(low_logit, gumbel[i] + logits[i] + normalized_qvalues[i]) + penalty[i]);
}
return score;
}
std::vector<float> generate_gumbel(float gumbel_scale, float gumbel_rng, int shape){
/*
Overview:
Generate gumbel vectors.
Arguments:
- gumbel_scale: the scale of gumbel.
- gumbel_rng: the seed to generate gumbel.
- shape: the shape of gumbel vectors to be generated
Outputs:
- gumbel vectors.
*/
std::mt19937 gen(static_cast<unsigned int>(gumbel_rng));
std::extreme_value_distribution<float> d(0, 1);
std::vector<float> gumbel;
for (int i = 0;i < shape;i++)
gumbel.push_back(gumbel_scale * d(gen));
return gumbel;
}
} |