1 citations · 2 across the 4 of their papers we have counts for
5 papers
Modularized Reinforcement Learning on LLMs: From MDP Creation to Exploration and Learning
Zhao Yang, Yuxuan Jiang, Ting-Chih Chen +18
Reinforcement learning (RL) has become central to LLM post-training, yet the methods that dominate current pipelines, PPO and GRPO, represent only a narrow slice of what RL offers.…
Hybrid Sequence Modeling and Reinforced Verification for Controllable Target-Conditioned Decision Making
Yue Pei, Hongming Zhang, Chao Gao +7
Target-conditioned sequence models provide a simple interface for controllable offline decision making, but the requested target return can be an unreliable control signal, especia…
-DQN: Improving Deep Q-Learning By Evolving the Behavior
Hongming Zhang, Fengshuo Bai, Chenjun Xiao +3
While many sophisticated exploration methods have been proposed, their lack of generality and high computational cost often lead researchers to favor simpler methods like -greed…
ETGL-DDPG: A Deep Deterministic Policy Gradient Algorithm for Sparse Reward Continuous Control
Ehsan Futuhi, Shayan Karimi, Chao Gao +1
We consider deep deterministic policy gradient (DDPG) in the context of reinforcement learning with sparse rewards. To enhance exploration, we introduce a search procedure, \emph{$…
Monte Carlo Tree Search in the Presence of Transition Uncertainty
Farnaz Kohankhaki, Kiarash Aghakasiri, Hongming Zhang +3
Monte Carlo Tree Search (MCTS) is an immensely popular search-based framework used for decision making. It is traditionally applied to domains where a perfect simulation model of t…