74 lines
2.0 KiB
Python
74 lines
2.0 KiB
Python
"""
|
|
Entwickeln Sie einen Reinforcement Learning (RL) Agenten, der in
|
|
einem minimalistischen Pacman-Spiel (bereitgestellt auf meiner
|
|
Homepage) effektiv Punkte sammelt, während er dem Geist
|
|
ausweicht und somit vermeidet gefressen zu werden.
|
|
"""
|
|
|
|
import numpy as np
|
|
|
|
def q_init():
|
|
""" Fill every possible action in every state with a small value for initialization"""
|
|
|
|
# Configuration
|
|
NUM_ACTIONS = 4
|
|
INITIAL_Q_VALUE = 0.0 # Small value for initialization
|
|
|
|
s1_range = range(1, 9)
|
|
s2_range = range(1, 4)
|
|
s3_range = range(1, 9)
|
|
s4_range = range(1, 4)
|
|
s_constrained_values = {1, 4, 5, 8}
|
|
|
|
# The Q-Table dictionary
|
|
q_table = {}
|
|
|
|
# Iterate through all possible combinations of s1, s2, s3, s4
|
|
for s1 in s1_range:
|
|
for s2 in s2_range:
|
|
for s3 in s3_range:
|
|
for s4 in s4_range:
|
|
|
|
# Skip impossible states
|
|
if s2 == 2 and s1 not in s_constrained_values:
|
|
continue
|
|
if s4 == 2 and s3 not in s_constrained_values:
|
|
continue
|
|
|
|
# Assign all possible states a tuple of values
|
|
state_key = (s1, s2, s3, s4)
|
|
q_table[state_key] = [INITIAL_Q_VALUE] * NUM_ACTIONS
|
|
|
|
print(f"Total number of valid states initialized: {len(q_table)}") # debugging
|
|
# print(list(q_table.items())[:5]) # Uncomment to see the first 5 entries
|
|
return q_table
|
|
|
|
def epsilon_greedy(q, s, epsilon=0.9):
|
|
"""
|
|
Return which direction Pacman should move to
|
|
epsilon-greedy algorithm TBD
|
|
"""
|
|
a_val = max(q[s])
|
|
a = q[s].index(a_val)
|
|
|
|
return a
|
|
|
|
|
|
def take_action(s, a):
|
|
s_new = s
|
|
if a == 0:
|
|
s_new[0] -= 1
|
|
if a == 1:
|
|
s_new[0] += 1
|
|
if a == 2:
|
|
s_new[1] += 1
|
|
if a == 3:
|
|
s_new[1] -= 1
|
|
|
|
# Calculate fucking r
|
|
# include if there is a point on the field
|
|
r = 0
|
|
|
|
return s_new, r
|
|
|