Reinforcement Learning Framework

A Reinforcement Learning Framework is an agent-based interactive learning machine learning framework that can train agents through environment interactions by DeepMind.

• AKA: RL Framework, Reinforcement Learning Library, Agent Learning Framework.
• Context:
  • It can typically implement RL Algorithms through policy gradient methods.
  • It can typically manage Environment Interfaces through gym-compatible wrappers.
  • It can typically track Reward Signals through reward functions.
  • It can typically maintain Experience Buffers through replay memorys.
  • It can typically optimize Action Policys through value function approximators.
  • ...
  • It can often support Multi-Agent Training through parallel environments.
  • It can often enable Curriculum Learning through task progression.
  • It can often provide Exploration Strategys through epsilon-greedy mechanisms.
  • It can often handle Continuous Action Spaces through actor-critic architectures.
  • ...
  • It can range from being a Tabular Reinforcement Learning Framework to being a Deep Reinforcement Learning Framework, depending on its reinforcement learning function approximation complexity.
  • It can range from being a Single-Agent Reinforcement Learning Framework to being a Multi-Agent Reinforcement Learning Framework, depending on its reinforcement learning agent scope.
  • ...
  • It can integrate with Simulation Environments for agent training.
  • It can connect to GPU Clusters for parallel computation.
  • It can interface with Visualization Tools for training monitoring.
  • It can communicate with Hyperparameter Tuners for optimization search.
  • It can synchronize with Model Checkpoint Systems for training recovery.
  • ...
• Example(s):
  • General Reinforcement Learning Frameworks, such as:
    • Stable Baseline Frameworks, such as:
      • Stable Baselines3 by DLR-RM for reliable RL algorithm implementation.
      • CleanRL for single-file RL algorithm.
    • Modular RL Frameworks, such as:
      • Ray RLlib by Anyscale for scalable reinforcement learning.
      • Tianshou for modular PyTorch RL.
  • Specialized Reinforcement Learning Frameworks, such as:
    • Robotics RL Frameworks, such as:
      • OpenAI Gym by OpenAI for standardized RL environment.
      • Isaac Gym by NVIDIA for physics-based robot learning.
    • Game RL Frameworks, such as:
      • OpenSpiel by DeepMind for game theory research.
      • PettingZoo for multi-agent environment.
  • ...
• Counter-Example(s):
  • Supervised Learning Frameworks, which lack reward-based learning.
  • Unsupervised Learning Frameworks, which lack environment interaction.
  • Imitation Learning Frameworks, which lack exploration mechanism.
• See: Reinforcement Learning, Machine Learning Framework, Agent-Based System, Federated Learning Framework.

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