Alpha-Mini: Minichess Agent with Deep Reinforcement Learning
원문 초록
We train an agent to compete in the game of Gardner minichess, a downsized variation of chess played on a 5x5 board. We motivated and applied a SOTA actor-critic method Proximal Policy Optimization with Generalized Advantage Estimation. Our initial task centered around training the agent against a random agent. Once we obtained reasonable performance, we then adopted a version of iterative policy improvement adopted by AlphaGo to pit the agent against increasingly stronger versions of itself, and evaluate the resulting performance gain. The final agent achieves a near (.97) perfect win rate against a random agent. We also explore the effects of pretraining the network using a collection of positions obtained via self-play.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Deep Reinforcement Learning with Gradient Eligibility Traces
DR-SAC: Distributionally Robust Soft Actor-Critic for Reinforcement Learning under Uncertainty
Margin Trader: A Reinforcement Learning Framework for Portfolio Management with Margin and Constraints
Task Independent Capsule-Based Agents for Deep Q-Learning
A State Representation Dueling Network for Deep Reinforcement Learning
Drafting in Collectible Card Games via Reinforcement Learning
토론
이 논문과 연결된 포럼 글이 아직 없습니다.