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XuanCe

Tutorial:

  • Installation
  • Quick Start
  • Further Usage
  • Custom Environments
    • Single-Agent
    • Multi-Agent
  • Custom Algorithms
    • DRL
    • MARL
  • Custom Callback

Algorithms:

  • Single-Agent RL
    • DQN
    • Double DQN
    • Dueling DQN
    • Noisy DQN
    • PER DQN
    • C51
    • QR-DQN
    • DRQN
    • PG
    • NPG
    • A2C
    • PPO
    • PPO-KL
    • PPG
    • SAC
    • DDPG
    • TD3
    • P-DQN
    • MP-DQN
    • SP-DQN
  • Multi-Agent RL
    • IQL
    • VDN
    • QMIX
    • WQMIX
    • QTRAN
    • DCG
    • IDDPG
    • MADDPG
    • IAC
    • COMA
    • VDAC
    • IPPO
    • MAPPO
    • MFQ
    • MFAC
    • ISAC
    • MASAC
    • MATD3
    • IC3Net
  • Model-based RL
    • DreamerV2
    • DreamerV3
    • HarmonyDream
  • Constructive RL
    • CURL
    • SPR
    • DrQ
  • Offline RL
    • TD3BC

Benchmarks:

  • Start Benchmark
  • Benchmark Results
    • MuJoCo
    • Atari
    • SMAC
  • Add New Benchmark

APIs:

  • 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
  • engine
    • run_basic
    • run_drl
    • run_marl
    • run_sc2
    • run_football
    • run_competition
    • run_offlinerl
  • 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
    • 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
    • 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
    • utils

Development:

  • Github
  • Release Log
  • Contribute to XuanCe
  • Contribute to Docs (EN)
  • Contribute to Docs (CN)
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Model-based Reinforcement LearningΒΆ

  • DreamerV2 : Dreamer V2.

  • DreamerV3 : Dreamer V3.

  • HarmonyDream : Harmony Dream.

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DreamerV2
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Individual Controlled Continuous Communication Model (IC3Net)
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