マロン提出

.gitignore

@@ -1 +1,5 @@
 .DS_Store
+./lightning_logs
+json
+/python/obs.npy
+/python/actions.npy

.vscode/settings.json

@@ -0,0 +1,17 @@
+{
+  "cSpell.ignoreWords": [
+    "RIICHI",
+    "Shanten",
+    "TSUMO",
+    "argmax",
+    "dataloaders",
+    "inps",
+    "logdir",
+    "logit",
+    "optim",
+    "preds",
+    "proba",
+    "tenho",
+    "tgts"
+  ]
+}

docker/requirements.txt

@@ -3,3 +3,29 @@ tqdm
 python-socketio
 eventlet
 python-socketio[client]
+aiohttp==3.8.4
+aiosignal==1.3.1
+async-timeout==4.0.2
+attrs==22.2.0
+cloudpickle==2.2.1
+filelock==3.10.2
+frozenlist==1.3.3
+fsspec==2023.3.0
+gym==0.26.2
+gym-notices==0.0.8
+Jinja2==3.1.2
+lightning-utilities==0.8.0
+MarkupSafe==2.1.2
+mpmath==1.3.0
+multidict==6.0.4
+networkx==3.0
+packaging==23.0
+pytorch-lightning==2.0.0
+PyYAML==6.0
+torch
+torchaudio
+torchmetrics
+torchvision
+typing_extensions==4.5.0
+sympy==1.11.1
+yarl==1.8.2

python/convert.py

@@ -0,0 +1,27 @@
+from mjx import Observation, State, Action 
+import glob
+import numpy as np
+count = 0
+files = glob.glob("./json/json/*")
+obs_hist = []
+action_hist = []
+for file in files:
+  print("#" + str(count) + "Loading file....")
+  with open(file) as f:
+    lines = f.readlines()
+    for line in lines:
+      state = State(line)
+      for cpp_obs, cpp_act in state._cpp_obj.past_decisions():
+        obs = Observation._from_cpp_obj(cpp_obs)
+        feature = obs.to_features(feature_name="mjx-small-v0")
+        action = Action._from_cpp_obj(cpp_act)
+        action_idx = action.to_idx()
+        obs_hist.append(feature)
+        action_hist.append(action_idx)
+  count += 1
+  if count >= 1000:
+    break
+
+
+np.save("obs.npy", np.stack(obs_hist))
+np.save("actions.npy", np.array(action_hist, dtype=np.int32))

python/custom_client_riku0801.py

@@ -0,0 +1,77 @@
+import torch
+import mjx
+from torch import optim, nn, utils, Tensor
+import pytorch_lightning as pl
+import mjx.agents
+from client.agent import CustomAgentBase
+from client.client import SocketIOClient
+
+class MLP(pl.LightningModule):
+    def __init__(self, obs_size=544, n_actions=181, hidden_size=544):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(obs_size, hidden_size),
+            nn.ReLU(),
+            nn.Linear(hidden_size, hidden_size),
+            nn.ReLU(),
+            nn.Linear(hidden_size, n_actions),
+        )
+        self.loss_module = nn.CrossEntropyLoss()
+
+    def training_step(self, batch, batch_idx):
+        x, y = batch
+        preds = self.forward(x)
+        loss = self.loss_module(preds, y)
+        self.log("train_loss", loss)
+        return loss
+
+    def configure_optimizers(self):
+        optimizer = optim.Adam(self.parameters(), lr=1e-3)
+        return optimizer
+
+    def forward(self, x):
+        return self.net(x.float())
+
+model = MLP()
+model.load_state_dict(torch.load('./model_0.pth'))
+
+class MyAgent(CustomAgentBase):
+
+    def __init__(self) -> None:
+        super().__init__()
+
+    def custom_act(self, obs: mjx.Observation) -> mjx.Action:
+        legal_actions = obs.legal_actions()
+        if len(legal_actions) == 1:
+            return legal_actions[0]
+
+        for action in legal_actions:
+            if action.type() in [mjx.ActionType.TSUMO, mjx.ActionType.RON]:
+                return action
+            elif action.type() == mjx.ActionType.RIICHI:
+                return action
+
+        feature = obs.to_features(feature_name="mjx-small-v0")
+        with torch.no_grad():
+            action_logit = model(Tensor(feature.ravel()))
+        action_proba = torch.sigmoid(action_logit).numpy()
+
+        mask = obs.action_mask()
+        action_idx = (mask * action_proba).argmax()
+        return mjx.Action.select_from(action_idx, legal_actions)
+
+if __name__ == "__main__":
+    # 4人で対局する場合は，4つのSocketIOClientで同一のサーバーに接続する．
+    my_agent = MyAgent()  # 参加者が実装したプレイヤーをインスタンス化
+
+    sio_client = SocketIOClient(
+        ip='localhost',
+        port=5000,
+        namespace='/test',
+        query='secret',
+        agent=my_agent,  # プレイヤーの指定
+        room_id=123,  # 部屋のID．4人で対局させる時は，同じIDを指定する．
+        )
+    # SocketIO Client インスタンスを実行
+    sio_client.run()
+    sio_client.enter_room()

python/learning.py

@@ -0,0 +1,69 @@
+from torch import optim, nn, utils, Tensor
+import pytorch_lightning as pl
+import torch
+import mjx
+import mjx.agents
+import numpy as np
+from torch.utils.data import TensorDataset, DataLoader
+from client.agent import CustomAgentBase
+
+
+class MLP(pl.LightningModule):
+    def __init__(self, obs_size=544, n_actions=181, hidden_size=544):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(obs_size, hidden_size),
+            nn.ReLU(),
+            nn.Linear(hidden_size, hidden_size),
+            nn.ReLU(),
+            nn.Linear(hidden_size, n_actions),
+        )
+        self.loss_module = nn.CrossEntropyLoss()
+
+    def training_step(self, batch, batch_idx):
+        x, y = batch
+        preds = self.forward(x)
+        loss = self.loss_module(preds, y)
+        self.log("train_loss", loss)
+        return loss
+
+    def configure_optimizers(self):
+        optimizer = optim.Adam(self.parameters(), lr=1e-3)
+        return optimizer
+
+    def forward(self, x):
+        return self.net(x.float())
+
+inps = np.load("./obs.npy")
+tgts = np.load("./actions.npy")
+inps = inps.reshape(661069, 16*34)
+
+dataset = TensorDataset(torch.Tensor(inps), torch.LongTensor(tgts))
+loader = DataLoader(dataset, batch_size=2)
+
+model = MLP()
+trainer = pl.Trainer(max_epochs=1)
+trainer.fit(model=model, train_dataloaders=loader)
+torch.save(model.state_dict(), './model_0.pth')
+
+class MyAgent(CustomAgentBase):
+
+    def __init__(self) -> None:
+        super().__init__()
+
+    def act(self, obs: mjx.Observation) -> mjx.Action:
+        legal_actions = obs.legal_actions()
+        if len(legal_actions) == 1:
+            return legal_actions[0]
+
+        # 予測
+        feature = obs.to_features(feature_name="mjx-small-v0")
+        with torch.no_grad():
+            action_logit = model(Tensor(feature.ravel()))
+        action_proba = torch.sigmoid(action_logit).numpy()
+
+        # アクション決定
+        mask = obs.action_mask()
+        action_idx = (mask * action_proba).argmax()
+        return mjx.Action.select_from(action_idx, legal_actions)
+