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Agent

rl.core.Agent(processor=None)

Abstract base class for all implemented agents.

Each agent interacts with the environment (as defined by the Env class) by first observing the state of the environment. Based on this observation the agent changes the environment by performing an action.

Do not use this abstract base class directly but instead use one of the concrete agents implemented. Each agent realizes a reinforcement learning algorithm. Since all agents conform to the same interface, you can use them interchangeably.

To implement your own agent, you have to implement the following methods:

  • forward
  • backward
  • compile
  • load_weights
  • save_weights
  • layers

Arguments

  • processor (Processor instance): See Processor for details.

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Processor

rl.core.Processor()

Abstract base class for implementing processors.

A processor acts as a coupling mechanism between an Agent and its Env. This can be necessary if your agent has different requirements with respect to the form of the observations, actions, and rewards of the environment. By implementing a custom processor, you can effectively translate between the two without having to change the underlaying implementation of the agent or environment.

Do not use this abstract base class directly but instead use one of the concrete implementations or write your own.

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Env

rl.core.Env()

The abstract environment class that is used by all agents. This class has the exact same API that OpenAI Gym uses so that integrating with it is trivial. In contrast to the OpenAI Gym implementation, this class only defines the abstract methods without any actual implementation.

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Space

rl.core.Space()

Abstract model for a space that is used for the state and action spaces. This class has the exact same API that OpenAI Gym uses so that integrating with it is trivial.