Disentangling Controllable Object through Video Prediction Improves Visual Reinforcement Learning

21 Feb 2020 Yuanyi Zhong Alexander Schwing Jian Peng

In many vision-based reinforcement learning (RL) problems, the agent controls a movable object in its visual field, e.g., the player's avatar in video games and the robotic arm in visual grasping and manipulation. Leveraging action-conditioned video prediction, we propose an end-to-end learning framework to disentangle the controllable object from the observation signal... (read more)

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Methods used in the Paper

Experience Replay
Replay Memory
Double Q-learning
Off-Policy TD Control
Off-Policy TD Control
Double DQN
Q-Learning Networks
Dense Connections
Feedforward Networks
Q-Learning Networks