Merge pull request #235 from strivebfq/main

add smacv2

examples/smacv2/custom_vecinfo.py

@@ -0,0 +1,60 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# Copyright 2023 The OpenRL Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+""""""
+
+from collections import deque
+from functools import reduce
+from typing import Any, Dict
+
+import numpy as np
+
+from openrl.envs.vec_env.vec_info.episode_rewards_info import EPS_RewardInfo
+
+
+class SMACInfo(EPS_RewardInfo):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.win_history = deque(maxlen=100)
+
+    def statistics(self, buffer: Any) -> Dict[str, Any]:
+        info_dict = super().statistics(buffer)
+
+        """for step_info in self.infos:
+            for singe_env_info in step_info:
+                assert isinstance(singe_env_info, dict), "singe_env_info must be dict"
+
+                if "final_info" in singe_env_info.keys():
+                    assert (
+                        "game_state" in singe_env_info["final_info"].keys()
+                    ), "game_state must be in info"
+                    assert singe_env_info["final_info"]["game_state"] in [
+                        "win",
+                        "lose",
+                    ], "game_state in the final_info must be win or lose"
+                    self.win_history.append(
+                        singe_env_info["final_info"]["game_state"] == "win"
+                    )"""
+
+        if len(self.win_history) > 0:
+            info_dict["win_rate"] = np.mean(self.win_history)
+
+        dead_ratio = 1 - buffer.data.active_masks.sum() / reduce(
+            lambda x, y: x * y, list(buffer.data.active_masks.shape)
+        )
+        info_dict["dead_ratio"] = dead_ratio
+
+        return info_dict