Reinforcement Learning with TensorFlow and TF-Agents

TF-Agents is a library for reinforcement learning (RL) in TensorFlow (TF). It makes the design and implementation of various algorithms easier by providing a number of modular components corresponding to the core parts of an RL problem:

  • An agent operates in an environment and learns by processing signals received every time it chooses an action. In TF-Agents, an environment is typically implemented in Python and wrapped in a TF wrapper to enable efficient parallelization.
  • A policy maps an observation from the environment into a distribution over actions.
  • A driver executes a policy in an environment for a specified number of steps (also called episodes).
  • A replay buffer is used to store ...

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