Action Branching Architectures for Deep Reinforcement Learning
This addresses the problem of scaling reinforcement learning to high-dimensional action spaces for researchers and practitioners, offering a novel architectural solution with competitive performance.
The paper tackles the combinatorial explosion of action spaces in deep reinforcement learning for high-dimensional continuous control tasks by proposing a branching neural architecture with a shared decision module and independent branches per action dimension. The resulting Branching Dueling Q-Network (BDQ) scales linearly with action dimensionality and performs competitively against DDPG on challenging continuous control tasks.
Discrete-action algorithms have been central to numerous recent successes of deep reinforcement learning. However, applying these algorithms to high-dimensional action tasks requires tackling the combinatorial increase of the number of possible actions with the number of action dimensions. This problem is further exacerbated for continuous-action tasks that require fine control of actions via discretization. In this paper, we propose a novel neural architecture featuring a shared decision module followed by several network branches, one for each action dimension. This approach achieves a linear increase of the number of network outputs with the number of degrees of freedom by allowing a level of independence for each individual action dimension. To illustrate the approach, we present a novel agent, called Branching Dueling Q-Network (BDQ), as a branching variant of the Dueling Double Deep Q-Network (Dueling DDQN). We evaluate the performance of our agent on a set of challenging continuous control tasks. The empirical results show that the proposed agent scales gracefully to environments with increasing action dimensionality and indicate the significance of the shared decision module in coordination of the distributed action branches. Furthermore, we show that the proposed agent performs competitively against a state-of-the-art continuous control algorithm, Deep Deterministic Policy Gradient (DDPG).