9 citations · 14 across the 3 of their papers we have counts for
3 papers
cs.LG2024
Mitigating Suboptimality of Deterministic Policy Gradients in Complex Q-functions
Ayush Jain, Norio Kosaka, Xinhu Li +3
In reinforcement learning, off-policy actor-critic methods like DDPG and TD3 use deterministic policy gradients: the Q-function is learned from environment data, while the actor ma…
cs.LG2023★ 5 cited
Direct Preference-based Policy Optimization without Reward Modeling
Gaon An, Junhyeok Lee, Xingdong Zuo +3
Preference-based reinforcement learning (PbRL) is an approach that enables RL agents to learn from preference, which is particularly useful when formulating a reward function is ch…
cs.LG2020★ 9 cited
PlaNet of the Bayesians: Reconsidering and Improving Deep Planning Network by Incorporating Bayesian Inference
Masashi Okada, Norio Kosaka, Tadahiro Taniguchi
In the present paper, we propose an extension of the Deep Planning Network (PlaNet), also referred to as PlaNet of the Bayesians (PlaNet-Bayes). There has been a growing demand in…