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0.4.0 (2021-05-19)

  • The package now requires Python 3.8+. This allowed to upgrade some dependencies like gym>=0.17.2 and numpy>=1.22.2.
  • Used pythomata at version >=0.3.0.
  • Introduced better abstract classes and interfaces for supporting reward machines.
    • The base classes and interfaces can be found in temprl.reward_machines.base. AbstractRewardMachine is not coupled with pythomata.DFA, giving the developer more flexibility on how to implement its own reward machine. Analogously, AbstractRewardMachineSimulator is a pure interface for the simulation of a reward machine, which is ultimately used by the TemporalGoal class. Anyway, the library provides a concrete class RewardMachineSimulator(AbstractRewardMachineSimulator) which should satisfy most of the use cases regarding the RM simulator.
    • In temprl.reward_machines.automata, the library provides an implementation of AbstractRewardMachine, called RewardAutomaton, which is based on pythomata.DFA to represent the reward machine structure.
  • Introduced AbstractStepController, which allows to filter out certain fluents observations from the RM perspective. The library provides two implementations: StatelessStepController, which behaves the same regardless of the history of the trajectory, and StatefulStepController, which is based on a pythomata.DFA instance and accepts a fluents configuration if after reading it the simulation is in an accepting state.
  • Improved linting by adding Pylint checks.
  • Other misc fixes to minor issues.

0.3.0 (2021-06-20)

  • Simplify APIs of TemporalWrapper: remove feature_extractor and combine parameters, as well as reward shaping support. The reason is that these functionalities, in the OpenAI Gym "philosophy", should be delegated to other Gym wrappers, e.g. ObservationWrapper for combining the features and the automata states.
  • Remove flloat dependency. Since TemporalGoal now only requires a pythomata.DFA object, it is up to the user to decide how to generate the reward automaton.
  • Update dependencies to their latest version, e.g. pythomata.
  • The reset() method of the temporal wrapper now first resets the temporal goals, and then makes a step on each of them according to the fluents extracted from the environment's initial state. This is needed because otherwise the initial state of the wrapped environment is ignored.
  • The support for terminating conditions from the temporal goals is removed. Again, this is because the only job of the DFAs is to provide rewards according to the history of the episode; any other customization of the underlying envrionment, or the wrapper, must be done by using other wrappers.

0.2.3 (2020-09-24)

  • Let temporal wrapper initialization directly from DFA
  • Added 'combine' method to customize how to do the actual product between DFA states and the environment state space.
  • Added new linters.
  • Renewed documentation.

0.2.2 (2019-11-03)

  • Improved support for reward shaping.
  • Fixed minor bugs and issues.

0.2.1 (2019-11-03)

  • Improved testing and continuous integration.
  • Fixed minor bugs and issues.

0.2.0 (2019-07-13)

  • Main refactoring of the package.

0.1.0 (2018-04-04)

  • First release on PyPI.