rename

.gitignore

@@ -4,7 +4,7 @@
 logs/
 dist/
 .eggs/
-xuanpolicy.egg-info/
+xuance.egg-info/
 models/
 *.sh
 .VSCodeCounter/

LICENSE.txt

@@ -1,5 +1,5 @@
 MIT License
-Copyright (c) 2018 XuanPolicy contributors
+Copyright (c) 2018 XuanCe contributors
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal
 in the Software without restriction, including without limitation the rights

README.md

@@ -1,13 +1,13 @@
 ![logo](./docs/source/figures/logo_1.png)
 
-# XuanPolicy: A Comprehensive and Unified Deep Reinforcement Learning Library
+# XuanCe: A Comprehensive and Unified Deep Reinforcement Learning Library
 
-[![PyPI](https://img.shields.io/pypi/v/xuanpolicy)](https://pypi.org/project/xuanpolicy/)
-[![Documentation Status](https://readthedocs.org/projects/xuanpolicy/badge/?version=latest)](https://xuanpolicy.readthedocs.io/en/latest/?badge=latest)
-![GitHub](https://img.shields.io/github/license/agi-brain/xuanpolicy)
-![GitHub Repo stars](https://img.shields.io/github/stars/agi-brain/xuanpolicy?style=social)
-![GitHub forks](https://img.shields.io/github/forks/agi-brain/xuanpolicy?style=social)
-![GitHub watchers](https://img.shields.io/github/watchers/agi-brain/xuanpolicy?style=social)
+[![PyPI](https://img.shields.io/pypi/v/xuance)](https://pypi.org/project/xuance/)
+[![Documentation Status](https://readthedocs.org/projects/xuance/badge/?version=latest)](https://xuance.readthedocs.io/en/latest/?badge=latest)
+![GitHub](https://img.shields.io/github/license/agi-brain/xuance)
+![GitHub Repo stars](https://img.shields.io/github/stars/agi-brain/xuance?style=social)
+![GitHub forks](https://img.shields.io/github/forks/agi-brain/xuance?style=social)
+![GitHub watchers](https://img.shields.io/github/watchers/agi-brain/xuance?style=social)
 
 [![PyTorch](https://img.shields.io/badge/PyTorch-%3E%3D1.13.0-red)](https://pytorch.org/get-started/locally/)
 [![TensorFlow](https://img.shields.io/badge/TensorFlow-%3E%3D2.6.0-orange)](https://www.tensorflow.org/install)
@@ -18,7 +18,7 @@
 [![gymnasium](https://img.shields.io/badge/gymnasium-%3E%3D0.28.1-blue)](https://www.gymlibrary.dev/)
 [![pettingzoo](https://img.shields.io/badge/PettingZoo-%3E%3D1.23.0-blue)](https://pettingzoo.farama.org/)
 
-**XuanPolicy** is an open-source ensemble of Deep Reinforcement Learning (DRL) algorithm implementations.
+**XuanCe** is an open-source ensemble of Deep Reinforcement Learning (DRL) algorithm implementations.
 
 We call it as **Xuan-Ce (玄策)** in Chinese. 
 "**Xuan (玄)**" means incredible and magic box, "**Ce (策)**" means policy.
@@ -34,9 +34,9 @@ We expect it to be compatible with multiple deep learning toolboxes(
 **[MindSpore](https://www.mindspore.cn/en)**),
 and hope it can really become a zoo full of DRL algorithms.
 
-| **[Full Documentation](https://xuanpolicy.readthedocs.io/en/latest)** |
-  **[中文文档](https://xuanpolicy.readthedocs.io/zh/latest/)** |
-  **[OpenI (启智社区)](https://openi.pcl.ac.cn/OpenRelearnware/XuanPolicy)** |
+| **[Full Documentation](https://xuance.readthedocs.io/en/latest)** |
+  **[中文文档](https://xuance.readthedocs.io/zh/latest/)** |
+  **[OpenI (启智社区)](https://openi.pcl.ac.cn/OpenRelearnware/XuanCe)** |
   **[XuanCe (Mini version)](https://github.com/wzcai99/xuance)** |
 
 ## Currently Included Algorithms
@@ -242,10 +242,10 @@ StarCraft Multi-Agentt Challenge.
 
 The library can be run at Linux, Windows, MacOS, and EulerOS, etc.
 
-Before installing **XuanPolicy**, you should install [Anaconda](https://www.anaconda.com/download) to prepare a python environment.
+Before installing **XuanCe**, you should install [Anaconda](https://www.anaconda.com/download) to prepare a python environment.
 (Note: select a proper version of Anaconda from [**here**](https://repo.anaconda.com/archive/).)
 
-After that, open a terminal and install **XuanPolicy** by the following steps.
+After that, open a terminal and install **XuanCe** by the following steps.
 
 **Step 1**: Create a new conda environment (python>=3.7 is suggested):
 
@@ -262,14 +262,14 @@ conda activate xpolicy
 **Step 3**: Install the library:
 
 ```commandline
-pip install xuanpolicy
+pip install xuance
 ```
 
-This command does not include the dependencies of deep learning toolboxes. To install the **XuanPolicy** with 
-deep learning tools, you can type `pip install xuanpolicy[torch]` for [PyTorch](https://pytorch.org/get-started/locally/),
-`pip install xuanpolicy[tensorflow]` for [TensorFlow2](https://www.tensorflow.org/install),
-`pip install xuanpolicy[mindspore]` for [MindSpore](https://www.mindspore.cn/install/en),
-and `pip install xuanpolicy[all]` for all dependencies.
+This command does not include the dependencies of deep learning toolboxes. To install the **XuanCe** with 
+deep learning tools, you can type `pip install xuance[torch]` for [PyTorch](https://pytorch.org/get-started/locally/),
+`pip install xuance[tensorflow]` for [TensorFlow2](https://www.tensorflow.org/install),
+`pip install xuance[mindspore]` for [MindSpore](https://www.mindspore.cn/install/en),
+and `pip install xuance[all]` for all dependencies.
 
 Note: Some extra packages should be installed manually for further usage. 
 
@@ -280,7 +280,7 @@ Note: Some extra packages should be installed manually for further usage.
 #### Train a Model
 
 ```python
-import xuanpolicy as xp
+import xuance as xp
 
 runner = xp.get_runner(method='dqn', 
                        env='classic_control', 
@@ -292,7 +292,7 @@ runner.run()
 #### Test the Model
 
 ```python
-import xuanpolicy as xp
+import xuance as xp
 
 runner_test = xp.get_runner(method='dqn', 
                             env='classic_control', 
@@ -485,8 +485,8 @@ $ tensorboard --logdir ./logs/dqn/torch/CartPole-v0
 [//]: # (| micro_focus      |             |             |             |             |             |      |      |       |)
 
 ```
-@misc{XuanPolicy2023,
-    title={XuanPolicy: A Comprehensive and Unified Deep Reinforcement Learning Library},
+@misc{XuanCe2023,
+    title={XuanCe: A Comprehensive and Unified Deep Reinforcement Learning Library},
     author={Wenzhang Liu, Wenzhe Cai, Kun Jiang, Guangran Cheng, Yuanda Wang, 
     Jiawei Wang, Jingyu Cao, Lele Xu, Chaoxu Mu, Changyin Sun},
     publisher = {GitHub},

benchmark.py

@@ -1,5 +1,5 @@
 import argparse
-from xuanpolicy import get_runner
+from xuance import get_runner
 
 
 def parse_args():

benchmark_marl.py

@@ -1,5 +1,5 @@
 import argparse
-from xuanpolicy import get_runner
+from xuance import get_runner
 
 
 def parse_args():

demo.py

@@ -1,5 +1,5 @@
 import argparse
-from xuanpolicy import get_runner
+from xuance import get_runner
 
 
 def parse_args():

demo_marl.py

@@ -1,7 +1,7 @@
 import argparse
 
-import xuanpolicy.torch.agents
-from xuanpolicy import get_runner
+import xuance.torch.agents
+from xuance import get_runner
 
 
 def parse_args():

docs/build/html/_sources/documents/api/agents/drl/a2c.rst.txt

@@ -8,12 +8,12 @@ A2C_Agent
 **PyTorch:**
 
 .. py:class:: 
-  xuanpolicy.torch.agent.policy_gradient.a2c_agent.A2C_Agent(config, envs, policy, optimizer, scheduler, device)
+  xuance.torch.agent.policy_gradient.a2c_agent.A2C_Agent(config, envs, policy, optimizer, scheduler, device)
 
   :param config: Provides hyper parameters.
   :type config: Namespace
   :param envs: The vectorized environments.
-  :type envs: xuanpolicy.environments.vector_envs.vector_env.VecEnv
+  :type envs: xuance.environments.vector_envs.vector_env.VecEnv
   :param policy: The policy that provides actions and values.
   :type policy: nn.Module
   :param optimizer: The optimizer that updates the parameters.
@@ -24,7 +24,7 @@ A2C_Agent
   :type device: str, int, torch.device
 
 .. py:function:: 
-  xuanpolicy.torch.agent.policy_gradient.a2c_agent.A2C_Agent._action(obs)
+  xuance.torch.agent.policy_gradient.a2c_agent.A2C_Agent._action(obs)
 
   Calculate actions according to the observations.
 
@@ -34,15 +34,15 @@ A2C_Agent
   :rtype: np.ndarray, np.ndarray
 
 .. py:function:: 
-  xuanpolicy.torch.agent.policy_gradient.a2c_agent.A2C_Agent.train(train_steps)
+  xuance.torch.agent.policy_gradient.a2c_agent.A2C_Agent.train(train_steps)
 
   Train the A2C agent.
 
   :param train_steps: The number of steps for training.
   :type train_steps: int
 
 .. py:function:: 
-  xuanpolicy.torch.agent.policy_gradient.a2c_agent.A2C_Agent.test(env_fn, test_episodes)
+  xuance.torch.agent.policy_gradient.a2c_agent.A2C_Agent.test(env_fn, test_episodes)
 
   Test the trained model.
 
@@ -79,7 +79,7 @@ Source Code
 
         import numpy as np
 
-        from xuanpolicy.torch.agents import *
+        from xuance.torch.agents import *
 
 
         class A2C_Agent(Agent):

docs/build/html/_sources/documents/api/agents/drl/basic_drl_class.rst.txt

@@ -1,38 +1,38 @@
 Agent
 =======================
 
-To create a new Agent, you should build a class inherit from ``xuanpolicy.torch.agents.agent.Agent`` , ``xuanpolicy.tensorflow.agents.agent.Agent``, or ``xuanpolicy.mindspore.agents.agent.Agent``.
+To create a new Agent, you should build a class inherit from ``xuance.torch.agents.agent.Agent`` , ``xuance.tensorflow.agents.agent.Agent``, or ``xuance.mindspore.agents.agent.Agent``.
 
 **PyTorch:**
 
 .. py:class:: 
-   xuanpolicy.torch.agents.agent.Agent(config, envs, policy, memory, learner, device, log_dir, model_dir)
+   xuance.torch.agents.agent.Agent(config, envs, policy, memory, learner, device, log_dir, model_dir)
 
    :param config: Provides hyper parameters.
    :type config: Namespace
    :param envs: The vectorized environments.
-   :type envs: xuanpolicy.environments.vector_envs.vector_env.VecEnv
+   :type envs: xuance.environments.vector_envs.vector_env.VecEnv
    :param policy: The policy that provides actions and values.
    :type policy: nn.Module
    :param memory: Experice replay buffer.
-   :type memory: xuanpolicy.common.memory_tools.Buffer
+   :type memory: xuance.common.memory_tools.Buffer
    :param learner: The learner that updates parameters of policy.
-   :type learner: xuanpolicy.torch.learner.Learner
+   :type learner: xuance.torch.learner.Learner
    :param device: Choose CPU or GPU to train the model.
    :type device: str, int, torch.device
    :param log_dir: The directory of log file, default is "./logs/".
    :type log_dir: str
    :param model_dir: The directory of model file, default is "./models/".
    :type model_dir: str
 
-.. py:function:: xuanpolicy.torch.agents.agent.Agent.save_model(model_name)
+.. py:function:: xuance.torch.agents.agent.Agent.save_model(model_name)
    
    Save the model.
 
    :param model_name: The model's name to be saved.
    :type model_name: str
 
-.. py:function:: xuanpolicy.torch.agents.agent.Agent.load_model(path, seed)
+.. py:function:: xuance.torch.agents.agent.Agent.load_model(path, seed)
 
    Load a model by specifying the ``path`` and ``seed`` .
 
@@ -41,7 +41,7 @@ To create a new Agent, you should build a class inherit from ``xuanpolicy.torch.
    :param seed: Select the seed that model was trained with if it exits.
    :type seed: int
 
-.. py:function:: xuanpolicy.torch.agents.agent.Agent.log_infos(info, x_index)
+.. py:function:: xuance.torch.agents.agent.Agent.log_infos(info, x_index)
 
    Visualize the training information via wandb or tensorboard.
 
@@ -50,7 +50,7 @@ To create a new Agent, you should build a class inherit from ``xuanpolicy.torch.
    :param x_index: Current step.
    :type x_index: int
 
-.. py:function:: xuanpolicy.torch.agents.agent.Agent.log_videos(info, fps x_index)
+.. py:function:: xuance.torch.agents.agent.Agent.log_videos(info, fps x_index)
 
    Visualize the interaction between agent and environment by uploading the videos with wandb or tensorboard.
 
@@ -61,7 +61,7 @@ To create a new Agent, you should build a class inherit from ``xuanpolicy.torch.
    :param x_index: Current step.
    :type x_index: int
 
-.. py:function:: xuanpolicy.torch.agents.agent.Agent._process_observation(observations)
+.. py:function:: xuance.torch.agents.agent.Agent._process_observation(observations)
 
    Normalize the original observations.
 
@@ -70,7 +70,7 @@ To create a new Agent, you should build a class inherit from ``xuanpolicy.torch.
    :return: The normalized observations.
    :rtype: numpy.ndarray
 
-.. py:function:: xuanpolicy.torch.agents.agent.Agent._process_reward(rewards)
+.. py:function:: xuance.torch.agents.agent.Agent._process_reward(rewards)
 
    Normalize the original rewards.
 
@@ -79,29 +79,29 @@ To create a new Agent, you should build a class inherit from ``xuanpolicy.torch.
    :return: The normalized observations rewards.
    :rtype: numpy.ndarray
 
-.. py:function:: xuanpolicy.torch.agents.agent.Agent._action(observations)
+.. py:function:: xuance.torch.agents.agent.Agent._action(observations)
    
    Get actions for executing according to the observations.
 
    :param observations: The original observations of agent.
    :type observations: numpy.ndarray
 
-.. py:function:: xuanpolicy.torch.agents.agent.Agent.train(steps)
+.. py:function:: xuance.torch.agents.agent.Agent.train(steps)
    
    Train the agents with ``steps`` steps.
 
    :param steps: The training steps.
    :type steps: int
 
-.. py:function:: xuanpolicy.torch.agents.agent.Agent.test(env_fn, steps)
+.. py:function:: xuance.torch.agents.agent.Agent.test(env_fn, steps)
    
    Test the agents.
 
    :param env_fn: The function of making environments.
    :param steps: The training steps.
    :type steps: int
 
-.. py:function:: xuanpolicy.torch.agents.agent.Agent.finish()
+.. py:function:: xuance.torch.agents.agent.Agent.finish()
    
    Finish the wandb or tensorboard.
 
@@ -113,18 +113,18 @@ To create a new Agent, you should build a class inherit from ``xuanpolicy.torch.
 **TensorFlow:**
 
 .. py:class:: 
-   xuanpolicy.tensorflowtensorflow.agent.agent.Agent(config, envs, policy, memory, learner, device, log_dir, model_dir)
+   xuance.tensorflowtensorflow.agent.agent.Agent(config, envs, policy, memory, learner, device, log_dir, model_dir)
 
    :param config: Provides hyper parameters.
    :type config: Namespace
    :param envs: The vectorized environments.
-   :type envs: xuanpolicy.environments.vector_envs.vector_env.VecEnv
+   :type envs: xuance.environments.vector_envs.vector_env.VecEnv
    :param policy: The policy that provides actions and values.
    :type policy: nn.Module
    :param memory: Experice replay buffer.
-   :type memory: xuanpolicy.common.memory_tools.Buffer
+   :type memory: xuance.common.memory_tools.Buffer
    :param learner: The learner that updates parameters of policy.
-   :type learner: xuanpolicy.tensorflow.learner.Learner
+   :type learner: xuance.tensorflow.learner.Learner
    :param device: Choose CPU or GPU to train the model.
    :type device: str
    :param log_dir: The directory of log file, default is "./logs/".
@@ -140,16 +140,16 @@ To create a new Agent, you should build a class inherit from ``xuanpolicy.torch.
 **MindSpore:**
 
 .. py:class:: 
-   xuanpolicy.mindsporetensorflow.agent.agent.Agent(envs, policy, memory, learner, device, log_dir, model_dir)
+   xuance.mindsporetensorflow.agent.agent.Agent(envs, policy, memory, learner, device, log_dir, model_dir)
 
    :param envs: The vectorized environments.
-   :type envs: xuanpolicy.environments.vector_envs.vector_env.VecEnv
+   :type envs: xuance.environments.vector_envs.vector_env.VecEnv
    :param policy: The policy that provides actions and values.
    :type policy: nn.Module
    :param memory: Experice replay buffer.
-   :type memory: xuanpolicy.common.memory_tools.Buffer
+   :type memory: xuance.common.memory_tools.Buffer
    :param learner: The learner that updates parameters of policy.
-   :type learner: xuanpolicy.mindspore.learner.Learner
+   :type learner: xuance.mindspore.learner.Learner
    :param device: Choose CPU or GPU to train the model.
    :type device: str
    :param log_dir: The directory of log file, default is "./logs/".