Deep Reinforcement Learning Based Robot Arm Manipulation with Efficient Training Data through Simulation
This work addresses training efficiency for robot arm manipulation tasks, but it is incremental as it builds on existing replay buffer methods.
The paper tackles the problem of inefficient training data in deep reinforcement learning for robot arm manipulation by proposing an adaptive and selective replay buffer update method, which improves policy performance in both simulation and real-world experiments.
Deep reinforcement learning trains neural networks using experiences sampled from the replay buffer, which is commonly updated at each time step. In this paper, we propose a method to update the replay buffer adaptively and selectively to train a robot arm to accomplish a suction task in simulation. The response time of the agent is thoroughly taken into account. The state transitions that remain stuck at the boundary of constraint are not stored. The policy trained with our method works better than the one with the common replay buffer update method. The result is demonstrated both by simulation and by experiment with a real robot arm.