FORK: A Forward-Looking Actor For Model-Free Reinforcement Learning
This addresses performance bottlenecks in reinforcement learning for continuous control tasks, offering incremental improvements to existing methods.
The paper tackles the problem of improving model-free reinforcement learning by proposing a forward-looking Actor (FORK) that integrates into Actor-Critic algorithms, resulting in significant performance gains on continuous control environments and solving Bipedal-WalkerHardcore in as few as four hours with a single GPU.
In this paper, we propose a new type of Actor, named forward-looking Actor or FORK for short, for Actor-Critic algorithms. FORK can be easily integrated into a model-free Actor-Critic algorithm. Our experiments on six Box2D and MuJoCo environments with continuous state and action spaces demonstrate significant performance improvement FORK can bring to the state-of-the-art algorithms. A variation of FORK can further solve Bipedal-WalkerHardcore in as few as four hours using a single GPU.