Spaces:
Sleeping
Sleeping
File size: 2,632 Bytes
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 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 | import gymnasium as gym
from gymnasium import spaces
import numpy as np
from env.mahoraga_env import MahoragaEnv
# === Encoding Maps ===
ATTACK_TYPE_ENCODING = {
"PHYSICAL": 0,
"CE": 1,
"TECHNIQUE": 2,
None: 0 # Default for initial state
}
SUBTYPE_ENCODING = {
"SLASH": 0,
"IMPACT": 1,
"PIERCE": 2,
"BLAST": 3,
"WAVE": 4,
"BEAM": 5,
"SPIKE": 6,
"DELAYED": 7,
"PATTERN": 8,
None: 0 # Default for initial state
}
ACTION_ENCODING = {
0: 0,
1: 1,
2: 2,
3: 3,
4: 4,
None: 0 # Default for initial state / wasted turn
}
class MahoragaGymEnv(gym.Env):
"""Gymnasium-compatible wrapper for MahoragaEnv."""
metadata = {"render_modes": []}
def __init__(self):
super().__init__()
self.env = MahoragaEnv()
self.action_space = spaces.Discrete(5)
self.observation_space = spaces.Dict({
"agent_hp": spaces.Box(low=0, high=1500, shape=(1,), dtype=np.int32),
"enemy_hp": spaces.Box(low=0, high=1500, shape=(1,), dtype=np.int32),
"resistances": spaces.Box(low=0, high=80, shape=(3,), dtype=np.int32),
"last_enemy_attack_type": spaces.Discrete(3),
"last_enemy_subtype": spaces.Discrete(9),
"last_action": spaces.Discrete(5),
"turn_number": spaces.Box(low=0, high=25, shape=(1,), dtype=np.int32)
})
def _encode_state(self, state):
"""Convert raw state dict to gymnasium-compatible observation."""
res = state["resistances"]
return {
"agent_hp": np.array([state["agent_hp"]], dtype=np.int32),
"enemy_hp": np.array([state["enemy_hp"]], dtype=np.int32),
"resistances": np.array(
[res["physical"], res["ce"], res["technique"]], dtype=np.int32
),
"last_enemy_attack_type": ATTACK_TYPE_ENCODING[state["last_enemy_attack_type"]],
"last_enemy_subtype": SUBTYPE_ENCODING[state["last_enemy_subtype"]],
"last_action": ACTION_ENCODING[state["last_action"]],
"turn_number": np.array([state["turn_number"]], dtype=np.int32)
}
def reset(self, seed=None, options=None):
super().reset(seed=seed)
state = self.env.reset()
observation = self._encode_state(state)
info = {}
return observation, info
def step(self, action):
state, reward, done, info = self.env.step(action)
observation = self._encode_state(state)
terminated = done
truncated = False
return observation, reward, terminated, truncated, info
|