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Release 0.9

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Main changes are detailed below:

New features -
* CARLA 0.7 simulator integration
* Human control of the game play
* Recording of human game play and storing / loading the replay buffer
* Behavioral cloning agent and presets
* Golden tests for several presets
* Selecting between deep / shallow image embedders
* Rendering through pygame (with some boost in performance)

API changes -
* Improved environment wrapper API
* Added an evaluate flag to allow convenient evaluation of existing checkpoints
* Improve frameskip definition in Gym

Bug fixes -
* Fixed loading of checkpoints for agents with more than one network
* Fixed the N Step Q learning agent python3 compatibility
This commit is contained in:
Itai Caspi
2017-12-19 19:27:16 +02:00
committed by GitHub
parent 11faf19649
commit 125c7ee38d
41 changed files with 1713 additions and 260 deletions
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2
agents/policy_optimization_agent.py
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@@ -58,7 +58,7 @@ class PolicyOptimizationAgent(Agent):
("steps", self.total_steps_counter),
("training iteration", self.training_iteration)
]),
prefix="Heatup" if self.in_heatup else "Training" if phase == RunPhase.TRAIN else "Testing"
prefix=phase
)
def update_episode_statistics(self, episode):