Tensorforce 0.6.5

Agents:

• Renamed agent argument reward_preprocessing to reward_processing, and in case of Tensorforce agent moved to reward_estimation[reward_processing]

Distributions:

• New categorical distribution argument skip_linear to not add the implicit linear logits layer

Environments:

• Support for multi-actor parallel environments via new function Environment.num_actors()
  • Runner uses multi-actor parallelism by default if environment is multi-actor
• New optional Environment function episode_return() which returns the true return of the last episode, if cumulative sum of environment rewards is not a good metric for runner display

Examples:

• New vectorized_environment.py and multiactor_environment.py script to illustrate how to setup a vectorized/multi-actor environment.