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c9d1b27 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | from env.mahoraga_env import MahoragaEnv
# Simulate: adapt-only agent (always action 0)
env = MahoragaEnv()
state = env.reset()
total_reward = 0
for t in range(25):
s, r, d, info = env.step(0)
total_reward += r
if d:
break
ahp = s["agent_hp"]
ehp = s["enemy_hp"]
print(f"ADAPT-ONLY: reward={total_reward:.2f}, turns={t+1}, won={ahp > ehp}, enemy_hp={ehp}")
# Simulate: smart agent (adapt 2x then strike, repeat)
env2 = MahoragaEnv()
state = env2.reset()
total_reward2 = 0
for t in range(25):
turn = t % 3
if turn < 2:
a = 0 # adapt PHYSICAL
else:
a = 3 # judgment strike
s, r, d, info = env2.step(a)
total_reward2 += r
if d:
break
ahp2 = s["agent_hp"]
ehp2 = s["enemy_hp"]
print(f"ADAPT+STRIKE: reward={total_reward2:.2f}, turns={t+1}, won={ahp2 > ehp2}, enemy_hp={ehp2}")
# Simulate: random agent (5 episodes)
import random
for ep in range(5):
env3 = MahoragaEnv()
env3.reset()
total = 0
attacks = 0
adapts = 0
for t in range(25):
a = random.randint(0, 4)
if a == 3:
attacks += 1
if a in [0, 1, 2]:
adapts += 1
s, r, d, info = env3.step(a)
total += r
if d:
break
ahp = s["agent_hp"]
ehp = s["enemy_hp"]
print(f" RANDOM ep{ep+1}: reward={total:.2f}, turns={t+1}, won={ahp>ehp}, attacks={attacks}, adapts={adapts}")
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