Soft Decomposed Policy-Critic: Bridging the Gap for Effective Continuous Control with Discrete RL
This addresses the problem of dimensional explosion in continuous control for RL researchers and practitioners, offering a novel hybrid approach that bridges discrete and continuous methods.
The paper tackles the challenge of applying discrete reinforcement learning algorithms to continuous control problems by introducing the Soft Decomposed Policy-Critic (SDPC) architecture, which outperforms state-of-the-art continuous RL algorithms in tasks like Mujoco's Humanoid and Box2d's BipedalWalker.
Discrete reinforcement learning (RL) algorithms have demonstrated exceptional performance in solving sequential decision tasks with discrete action spaces, such as Atari games. However, their effectiveness is hindered when applied to continuous control problems due to the challenge of dimensional explosion. In this paper, we present the Soft Decomposed Policy-Critic (SDPC) architecture, which combines soft RL and actor-critic techniques with discrete RL methods to overcome this limitation. SDPC discretizes each action dimension independently and employs a shared critic network to maximize the soft $Q$-function. This novel approach enables SDPC to support two types of policies: decomposed actors that lead to the Soft Decomposed Actor-Critic (SDAC) algorithm, and decomposed $Q$-networks that generate Boltzmann soft exploration policies, resulting in the Soft Decomposed-Critic Q (SDCQ) algorithm. Through extensive experiments, we demonstrate that our proposed approach outperforms state-of-the-art continuous RL algorithms in a variety of continuous control tasks, including Mujoco's Humanoid and Box2d's BipedalWalker. These empirical results validate the effectiveness of the SDPC architecture in addressing the challenges associated with continuous control.