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教程

  • 安装教程
  • 快速开始
  • 进一步使用
  • 自定义环境
    • 单智能体
    • 多智能体
  • 自定义算法
    • DRL
    • MARL
  • 自定义回调函数

算法:

  • 单智能体强化学习
    • DQN
    • Double DQN
    • Dueling DQN
    • Noisy DQN
    • PER DQN
    • C51
    • QR-DQN
    • DRQN
    • PG
    • NPG
    • A2C
    • PPO-KL
    • PPO-Clip
    • PPG
    • SAC
    • DDPG
    • TD3
    • P-DQN
    • MP-DQN
    • SP-DQN
  • 多智能体强化学习
    • IQL
    • VDN
    • QMIX
    • WQMIX
    • QTRAN
    • DCG
    • IDDPG
    • MADDPG
    • IAC
    • COMA
    • VDAC
    • IPPO
    • MAPPO
    • MFQ
    • MFAC
    • ISAC
    • MASAC
    • MATD3
    • IC3Net
  • 基于模型强化学习
    • DreamerV2
    • DreamerV3
    • HarmonyDream
  • 对比强化学习
    • CURL
    • SPR
    • DrQ
  • 离线强化学习
    • TD3BC

基准

  • 启动基准测试
  • 基准测试结果
    • MuJoCo
    • Atari
    • SMAC
  • 添加新的基准

接口:

  • common
    • tuning_tools
    • callback
    • common_tools
    • memory_offline
    • memory_tools
    • memory_tools_marl
    • offline_util
    • segtree_tool
    • statistic_tools
  • configs
    • Basic Configurations
    • Configuration Examples
    • Custom Configurations
  • environments
    • single_agent_env
      • Gymnasium
      • MiniGrid
      • MetaDrive
      • Gym-Platform
    • multi_agent_env
      • MPE
      • RWARE
      • SMAC
      • Football
      • Drones
      • Magent2
    • vectorization
      • Dummy Vectorization
      • Subprocess Vectorization
      • Basic Class
      • Utils
    • utils
      • Base Class
      • Wrappers
  • torch
    • agents
      • base
      • contrastive_unsupervised_rl
      • core
      • model_based_rl
      • multi_agent_rl
      • offline_rl
      • policy_gradient
      • qlearning_family
    • communications
    • learners
      • learner
      • contrastive_unsupervised_rl
      • model_based
      • multi_agent_rl
      • offline
      • policy_gradient
      • qlearning_family
    • policies
    • representations
    • runners
    • utils
  • tensorflow
    • agents
      • base
      • contrastive_unsupervised_rl
      • core
      • model_based_rl
      • multi_agent_rl
      • offline_rl
      • policy_gradient
      • qlearning_family
    • communications
    • learners
      • learner
      • contrastive_unsupervised_rl
      • model_based
      • multi_agent_rl
      • offline
      • policy_gradient
      • qlearning_family
    • policies
    • representations
    • runners
    • utils
  • mindspore
    • agents
      • base
      • contrastive_unsupervised_rl
      • core
      • model_based_rl
      • multi_agent_rl
      • offline_rl
      • policy_gradient
      • qlearning_family
    • communications
    • learners
      • learner
      • contrastive_unsupervised_rl
      • model_based
      • multi_agent_rl
      • offline
      • policy_gradient
      • qlearning_family
    • policies
    • representations
    • runners
    • utils

玄策开发

  • Github
  • 版本发布日志
  • 贡献指南
  • 文档贡献(英文)
  • 文档贡献(中文)
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common¶

Within the common module, various resuable tools are developed, which are independent of the choice of DL toolbox. These tools encompass common tools, memory tools for DRL and MARL, and statistic tools, etc.

  • tuning_tools.

  • callback.

  • common_tools.

  • memory_offline.

  • memory_tools.

  • memory_tools_marl.

  • offline_util.

  • segtree_tool.

  • statistic_tools.

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Tuning Tools
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How to Add a New Benchmark
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