Beta Version 1.7.0

Added

• Added all the recurrent version of deep reinforcement learning models from the “Models” section to the “RecurrentModels” section. The models include:

  • RecurrentVanillaPolicyGradient

  • RecurrentActorCritic

  • RecurrentAdvantageActorCritic

  • RecurrentSoftActorCritic

  • RecurrentProximalPolicyOptimization

  • RecurrentProximalPolicyOptimizationClip

  • RecurrentDeepDeterministicPolicyGradient

  • RecurrentTwinDelayedDeepDeterministicPolicyGradient

  • RecurrentREINFORCE

  • RecurrentMonteCarloControl

  • RecurrentOffPolicyMonteCarloControl

  • RecurrentDeepQLearning

  • RecurrentDeepStateActionRewardStateAction

  • RecurrentDeepExpectedStateActionRewardStateAction

  • RecurrentDeepClippedDoubleQLearning

  • RecurrentDeepDoubleQLearningV1

  • RecurrentDeepDoubleQLearningV2

  • RecurrentDeepDoubleStateActionRewardStateActionV1

  • RecurrentDeepDoubleStateActionRewardStateActionV2

  • RecurrentDeepDoubleExpectedStateActionRewardStateActionV1

  • RecurrentDeepDoubleExpectedStateActionRewardStateActionV2

Changes

• Refactored all the codes under the “Container” section.

• Refactored IterativeTrainingWrapper codes under the “Utilities” section.

• Improved the first derivative tensor calculations for ConstantPadding, ReplicationPadding and ReflectionPadding under the “PaddingBlocks” section.