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test also return actions

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jens
2025-04-02 15:55:52 +02:00
parent 9b26f6975f
commit e0c9ccba8c
1 changed files with 12 additions and 10 deletions
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k-bandits.py
+12 -10
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@@ -13,7 +13,8 @@ def bandit(a: int, bias: np.array):
def test(_k_arms: int, _episode_len: int, _bandit: Callable, _param: tuple[float, float]) -> np.array: def test(_k_arms: int, _episode_len: int, _bandit: Callable, _param: tuple[float, float]) -> np.array:
_epsilon = _param[0] # Anti-greediness (ability to explore) _epsilon = _param[0] # Anti-greediness (ability to explore)
_rho = _param[1] # reduce epsilon with age _rho = _param[1] # reduce epsilon with age
_r_mean = [] _r_list = []
_a_list = []
# Init Q and N # Init Q and N
_qu = np.zeros(_k_arms) _qu = np.zeros(_k_arms)
@@ -24,26 +25,26 @@ def test(_k_arms: int, _episode_len: int, _bandit: Callable, _param: tuple[float
# Calc a_expl in advance # Calc a_expl in advance
a_expl = np.random.randint(low=0, high=_k_arms, size=_episode_len) a_expl = np.random.randint(low=0, high=_k_arms, size=_episode_len)
for j in range(0, _episode_len): for j in range(0, _episode_len):
a = a_expl[j] _a = a_expl[j]
# choose action # choose action
if z[j] > _epsilon: if z[j] > _epsilon:
# Choose best action # Choose best action
a = np.argmax(_qu) _a = np.argmax(_qu)
# get reward from bandit # get reward from bandit
r = _bandit(a) _r = _bandit(_a)
_nu[a] = _nu[a] + 1 _nu[_a] = _nu[_a] + 1
_qu[a] = _qu[a] + (r - _qu[a]) / _nu[a] _qu[_a] = _qu[_a] + (_r - _qu[_a]) / _nu[_a]
# Reduce tendency to explore with number of steps (or with age for humans) # Reduce tendency to explore with number of steps (or with age for humans)
_epsilon = _epsilon * (1 - _rho) _epsilon = _epsilon * (1 - _rho)
# Statistics # Statistics
_r_mean.append(r) _a_list.append(_a)
_r_list.append(_r)
return np.array(_r_mean) return np.array(_r_list), np.array(_a_list)
if __name__ == '__main__': if __name__ == '__main__':
@@ -64,7 +65,8 @@ if __name__ == '__main__':
# -> expected reward q*(a) # -> expected reward q*(a)
ql_star = np.random.uniform(-highest_reward, +highest_reward, k_arms) ql_star = np.random.uniform(-highest_reward, +highest_reward, k_arms)
r_mean += test(_k_arms=k_arms, _episode_len=episode_len, _bandit=lambda a: bandit(a, ql_star), _param=param) r_list, a_list = test(_k_arms=k_arms, _episode_len=episode_len, _bandit=lambda a: bandit(a, ql_star), _param=param)
r_mean += r_list
pl.plot(r_mean/num_realisations) pl.plot(r_mean/num_realisations)
legend.append(f"Param {param}") legend.append(f"Param {param}")