dirty yet working goap
This commit is contained in:
parent
30407477ea
commit
19ea8d1eaa
2 changed files with 393 additions and 17 deletions
336
s2ga.hpp
336
s2ga.hpp
|
|
@ -1,6 +1,7 @@
|
|||
#include <random>
|
||||
#include <bitset>
|
||||
#include <magic_enum/magic_enum.hpp>
|
||||
#include <stdio.h>
|
||||
#include <type_traits>
|
||||
#include <iostream>
|
||||
#include <format>
|
||||
|
|
@ -99,7 +100,7 @@ namespace s2ga
|
|||
bool preconds_met(const std::bitset<N>& state) const
|
||||
{
|
||||
return ((state & positive_preconds) == positive_preconds) &&
|
||||
(state & ~negative_preconds).none();
|
||||
(state & negative_preconds).none();
|
||||
}
|
||||
|
||||
std::bitset<N> apply(const std::bitset<N>& state) const
|
||||
|
|
@ -150,8 +151,333 @@ namespace s2ga
|
|||
template<typename E>
|
||||
requires std::is_enum_v<E>
|
||||
using action_list = std::vector<action<E>>;
|
||||
}; // namespace s2ga
|
||||
|
||||
inline void foo()
|
||||
{
|
||||
}
|
||||
template<typename E>
|
||||
requires std::is_enum_v<E>
|
||||
struct goal
|
||||
{
|
||||
static constexpr size_t N = magic_enum::enum_count<E>();
|
||||
std::bitset<N> positive_goals;
|
||||
std::bitset<N> negative_goals;
|
||||
|
||||
int distance(const std::bitset<N>& state)
|
||||
{
|
||||
/*
|
||||
*V000111
|
||||
*P111100
|
||||
*&000100
|
||||
*^111000 <- distance 3
|
||||
*/
|
||||
/*V000111
|
||||
*P000000
|
||||
*&000000
|
||||
*^000000 <- distance 0
|
||||
*/
|
||||
/*V000111
|
||||
*P111111
|
||||
*&000111
|
||||
*^111000 <- distance 3
|
||||
*/
|
||||
|
||||
auto positive_bm = (state & positive_goals) ^ positive_goals;
|
||||
|
||||
/*
|
||||
*V000111
|
||||
*N111100
|
||||
*&000100 <- distance 1
|
||||
*/
|
||||
/*
|
||||
*V000111
|
||||
*N000000
|
||||
*&000000 <- distance 0
|
||||
*/
|
||||
auto negative_bm = state & negative_goals;
|
||||
|
||||
return positive_bm.count() + negative_bm.count();
|
||||
}
|
||||
};
|
||||
|
||||
template<typename E, int MaxDepth = 20>
|
||||
requires std::is_enum_v<E>
|
||||
class genetic_goap
|
||||
{
|
||||
static constexpr size_t N = magic_enum::enum_count<E>();
|
||||
|
||||
struct genotype
|
||||
{
|
||||
int distance(const genotype& g)
|
||||
{
|
||||
int d = 0;
|
||||
|
||||
for(int i = 0; i < MaxDepth; ++i)
|
||||
{
|
||||
if(action_ids[i] != g.action_ids[i])
|
||||
{
|
||||
d++;
|
||||
}
|
||||
}
|
||||
|
||||
return d;
|
||||
}
|
||||
|
||||
std::array<int, MaxDepth> action_ids;
|
||||
float fitness = 9999.0f;
|
||||
};
|
||||
|
||||
void evaluate(genotype& g)
|
||||
{
|
||||
float cost = 0.0f;
|
||||
|
||||
auto state = initial_state;
|
||||
|
||||
int valid_actions = 0;
|
||||
|
||||
int d = 99999;
|
||||
|
||||
for(int i = 0; i < MaxDepth; ++i)
|
||||
{
|
||||
auto aid = g.action_ids[i];
|
||||
|
||||
if(aid < 0)
|
||||
{
|
||||
continue;
|
||||
}
|
||||
|
||||
if(!actions[aid].preconds_met(state))
|
||||
{
|
||||
continue;
|
||||
}
|
||||
|
||||
state = actions[aid].apply(state);
|
||||
d = final_goal.distance(state);
|
||||
cost += actions[aid].cost;
|
||||
valid_actions++;
|
||||
|
||||
if(d == 0)
|
||||
{
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
if(valid_actions == 0)
|
||||
{
|
||||
g.fitness = 9999.0f;
|
||||
return;
|
||||
}
|
||||
|
||||
g.fitness = 9999.0f * d * float(valid_actions) + cost;
|
||||
//std::cout << std::format("{}({})", g.fitness, cost) << std::endl;
|
||||
}
|
||||
|
||||
void evaluate(std::vector<genotype>& g)
|
||||
{
|
||||
for(auto& i: g)
|
||||
{
|
||||
evaluate(i);
|
||||
}
|
||||
}
|
||||
|
||||
std::vector<genotype> generate_population(int size)
|
||||
{
|
||||
std::vector<genotype> population;
|
||||
population.reserve(size);
|
||||
|
||||
for(int i = 0; i < size; ++i)
|
||||
{
|
||||
population.emplace_back();
|
||||
|
||||
auto& d = population.back();
|
||||
|
||||
std::generate(std::begin(d.action_ids), std::end(d.action_ids),
|
||||
[&]()
|
||||
{
|
||||
return -1;
|
||||
});
|
||||
|
||||
const int ATTEMPTS = 4;
|
||||
|
||||
auto last_state = initial_state;
|
||||
|
||||
for(int j = 0; j < MaxDepth; ++j)
|
||||
{
|
||||
bool found = false;
|
||||
for(int attempt = 0; attempt < ATTEMPTS; attempt++)
|
||||
{
|
||||
int act_id = rng.random(0, int(actions.size()) - 1);
|
||||
auto& act = actions[act_id];
|
||||
if(act.preconds_met(last_state))
|
||||
{
|
||||
last_state = act.apply(last_state);
|
||||
d.action_ids[j] = act_id;
|
||||
found = true;
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
if(!found)
|
||||
{
|
||||
d.action_ids[j] = rng.random(-1, int(actions.size()) - 1);
|
||||
}
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
return population;
|
||||
}
|
||||
|
||||
action_list<E> to_action_list(const genotype& g)
|
||||
{
|
||||
action_list<E> result;
|
||||
auto state = initial_state;
|
||||
|
||||
int d = 9999;
|
||||
|
||||
for(int i = 0; i < MaxDepth; ++i)
|
||||
{
|
||||
auto aid = g.action_ids[i];
|
||||
|
||||
if(aid < 0)
|
||||
{
|
||||
continue;
|
||||
}
|
||||
|
||||
if(!actions[aid].preconds_met(state))
|
||||
{
|
||||
continue;
|
||||
}
|
||||
|
||||
state = actions[aid].apply(state);
|
||||
result.emplace_back(actions[aid]);
|
||||
|
||||
d = final_goal.distance(state);
|
||||
|
||||
if(d == 0)
|
||||
{
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
std::vector<genotype> elitism(int N, const std::vector<genotype>& p)
|
||||
{
|
||||
std::vector<genotype> result;
|
||||
result.reserve(N);
|
||||
|
||||
for(int i = 0; i < N; ++i)
|
||||
{
|
||||
result.emplace_back(p[i]);
|
||||
}
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
const genotype& tornament_selection(const std::vector<genotype>& p, int k = 8)
|
||||
{
|
||||
int id = rng.random(0, int(p.size()) - 1);
|
||||
|
||||
for(int i = 1; i < k; ++i)
|
||||
{
|
||||
int nid = rng.random(0, int(p.size()) - 1);
|
||||
|
||||
if(p[nid].fitness < p[id].fitness)
|
||||
{
|
||||
id = nid;
|
||||
}
|
||||
}
|
||||
|
||||
return p[id];
|
||||
}
|
||||
|
||||
genotype crossover(const genotype& a, const genotype& b)
|
||||
{
|
||||
genotype result;
|
||||
|
||||
|
||||
int split_index = rng.random(0, MaxDepth - 1);
|
||||
for(int i = 0; i < MaxDepth; ++i)
|
||||
{
|
||||
bool from_a = i < split_index;
|
||||
result.action_ids[i] = from_a ? a.action_ids[i] : b.action_ids[i];
|
||||
}
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
void mutate(genotype& g)
|
||||
{
|
||||
int index = rng.random(0, MaxDepth - 1);
|
||||
g.action_ids[index] = rng.random(-1, int(actions.size() - 1));
|
||||
}
|
||||
public:
|
||||
action_list<E> plan(int population_size, int max_attempts)
|
||||
{
|
||||
auto population = generate_population(population_size);
|
||||
|
||||
int attempts = 0;
|
||||
|
||||
|
||||
float last_f = 9999999.0f;
|
||||
|
||||
while(attempts < max_attempts)
|
||||
{
|
||||
evaluate(population);
|
||||
std::sort(population.begin(), population.end(),
|
||||
[](const genotype& a, const genotype& b)
|
||||
{
|
||||
return a.fitness < b.fitness;
|
||||
});
|
||||
|
||||
if(population[0].fitness < last_f)
|
||||
{
|
||||
last_f = population[0].fitness;
|
||||
attempts = 0;
|
||||
}
|
||||
else
|
||||
{
|
||||
attempts++;
|
||||
}
|
||||
|
||||
auto new_pop = elitism(3, population);
|
||||
|
||||
while(new_pop.size() < population.size())
|
||||
{
|
||||
auto solution = crossover(tornament_selection(population),
|
||||
tornament_selection(population));
|
||||
|
||||
if(rng.random(0, 100) <= 5)
|
||||
{
|
||||
mutate(solution);
|
||||
}
|
||||
|
||||
new_pop.emplace_back(solution);
|
||||
}
|
||||
|
||||
population = new_pop;
|
||||
}
|
||||
|
||||
return to_action_list(population[0]);
|
||||
}
|
||||
|
||||
void set_possible_actions(const action_list<E>& list)
|
||||
{
|
||||
actions = list;
|
||||
}
|
||||
|
||||
void set_initial_state(const std::bitset<N>& state)
|
||||
{
|
||||
initial_state = state;
|
||||
}
|
||||
|
||||
void set_goal(const goal<E>& goal)
|
||||
{
|
||||
final_goal = goal;
|
||||
}
|
||||
private:
|
||||
action_list<E> actions;
|
||||
std::bitset<N> initial_state;
|
||||
goal<E> final_goal;
|
||||
lehmer64 rng;
|
||||
};
|
||||
}; // namespace s2ga
|
||||
|
|
|
|||
|
|
@ -23,6 +23,7 @@ TEST_CASE("(dummy)", "[test]")
|
|||
HAS_TOOLS,
|
||||
HAS_FABRIC,
|
||||
HAS_ANIMAL_SKIN,
|
||||
HAS_RAW_FOOD,
|
||||
IS_WET,
|
||||
HAS_FIRE,
|
||||
IS_RAINING
|
||||
|
|
@ -32,24 +33,30 @@ TEST_CASE("(dummy)", "[test]")
|
|||
{
|
||||
{
|
||||
.name = "COLLECT FRUITS",
|
||||
.cost = 2.0f,
|
||||
.cost = 20.0f,
|
||||
.positive_effects = bm<State>(HAS_FOOD),
|
||||
.negative_preconds = bm<State>(IS_RAINING),
|
||||
},
|
||||
{
|
||||
.name = "COLLECT FRUITS",
|
||||
.cost = 2.0f,
|
||||
.positive_effects = bm<State>(HAS_FOOD),
|
||||
.negative_preconds = bm<State>(IS_RAINING),
|
||||
.name = "EAT",
|
||||
.cost = 0.1f,
|
||||
.negative_effects = bm<State>(HUNGRY, HAS_FOOD),
|
||||
.positive_preconds = bm<State>(HAS_FOOD)
|
||||
},
|
||||
{
|
||||
.name = "MAKE FISH TRAP",
|
||||
.name = "TIME TO FISH",
|
||||
.cost = 0.5f,
|
||||
.positive_effects = bm<State>(HAS_FOOD),
|
||||
.negative_effects = bm<State>(HAS_WOOD),
|
||||
.positive_effects = bm<State>(HAS_RAW_FOOD),
|
||||
.positive_preconds = bm<State>(HAS_TOOLS),
|
||||
.negative_preconds = bm<State>(IS_WET),
|
||||
},
|
||||
{
|
||||
.name = "COOK",
|
||||
.cost = 0.25f,
|
||||
.positive_effects = bm<State>(HAS_FOOD),
|
||||
.negative_effects = bm<State>(HAS_RAW_FOOD),
|
||||
.positive_preconds = bm<State>(HAS_RAW_FOOD)
|
||||
},
|
||||
{
|
||||
.name = "BUILD HOUSE",
|
||||
.cost = 8.0f,
|
||||
|
|
@ -76,8 +83,7 @@ TEST_CASE("(dummy)", "[test]")
|
|||
{
|
||||
.name = "HUNT ANIMAL",
|
||||
.cost = 6.0f,
|
||||
.positive_effects = bm<State>(HAS_ANIMAL_SKIN, HAS_FOOD),
|
||||
.negative_effects = bm<State>(HUNGRY),
|
||||
.positive_effects = bm<State>(HAS_ANIMAL_SKIN, HAS_RAW_FOOD),
|
||||
.positive_preconds = bm<State>(HAS_TOOLS),
|
||||
.negative_preconds = bm<State>(IS_RAINING),
|
||||
},
|
||||
|
|
@ -99,12 +105,56 @@ TEST_CASE("(dummy)", "[test]")
|
|||
.cost = 5.0f,
|
||||
.negative_effects = bm<State>(IS_RAINING),
|
||||
.positive_preconds = bm<State>(IS_RAINING),
|
||||
},
|
||||
{
|
||||
.name = "PROCESS ANIMAL SKIN",
|
||||
.cost = 6.0f,
|
||||
.positive_effects = bm<State>(HAS_FABRIC),
|
||||
.negative_effects = bm<State>(HAS_ANIMAL_SKIN),
|
||||
.positive_preconds = bm<State>(HAS_ANIMAL_SKIN)
|
||||
},
|
||||
};
|
||||
|
||||
BENCHMARK("GOAPERS")
|
||||
{
|
||||
goal<State> goal;
|
||||
goal.negative_goals = bm<State>(HUNGRY);
|
||||
goal.positive_goals = bm<State>(HAS_HOUSE);
|
||||
|
||||
genetic_goap<State> goap;
|
||||
goap.set_initial_state(bm<State>(HUNGRY, IS_WET, IS_RAINING));
|
||||
goap.set_possible_actions(actions);
|
||||
goap.set_goal(goal);
|
||||
|
||||
for(int i = 0; i < 32; ++i)
|
||||
{
|
||||
auto plan = goap.plan(100, 5);
|
||||
(void)plan;
|
||||
}
|
||||
};
|
||||
|
||||
for(auto& action: actions)
|
||||
goal<State> goal;
|
||||
goal.negative_goals = bm<State>(HUNGRY);
|
||||
goal.positive_goals = bm<State>(HAS_HOUSE);
|
||||
|
||||
genetic_goap<State> goap;
|
||||
goap.set_initial_state(bm<State>(HUNGRY, IS_WET, IS_RAINING));
|
||||
goap.set_possible_actions(actions);
|
||||
goap.set_goal(goal);
|
||||
auto plan = goap.plan(100, 5);
|
||||
|
||||
float cost = 0.0f;
|
||||
for(auto& i: plan)
|
||||
{
|
||||
action.print();
|
||||
i.print();
|
||||
cost += i.cost;
|
||||
}
|
||||
|
||||
std::cout << std::format("[Total Cost: {}]", cost) << std::endl;
|
||||
|
||||
if(plan.size() == 0)
|
||||
{
|
||||
std::cout << "SAD" << std::endl;
|
||||
}
|
||||
}
|
||||
|
||||
|
|
|
|||
Loading…
Reference in a new issue