arXiv:2010.01652
공식 코드 · Papers with Code
FORK: A Forward-Looking Actor For Model-Free Reinforcement Learning
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Deep Reinforcement Learning with Gradient Eligibility Traces
AutoTriton: Automatic Triton Programming with Reinforcement Learning in LLMs
Aligning Humans and Robots via Reinforcement Learning from Implicit Human Feedback
High-Throughput Distributed Reinforcement Learning via Adaptive Policy Synchronization
Exploring the robustness of TractOracle methods in RL-based tractography
Generalized Adaptive Transfer Network: Enhancing Transfer Learning in Reinforcement Learning Across Domains
토론
이 논문과 연결된 포럼 글이 아직 없습니다.