103 citations · 104 across the 14 of their papers we have counts for
15 papers
Learning Hierarchical Skill Policies with Offline Quality-Diversity Reinforcement Learning
Tanachai Anakewat, Takayuki Osa, Tatsuya Harada
Recent studies investigate how to leverage pre-collected datasets to improve the policy performance and sample efficiency of RL. One promising approach to achieve this goal is to e…
Learning the Supports for Categorical Critic in Reinforcement Learning
Jen-Yen Chang, Takayuki Osa, Tatsuya Harada
Value functions are an essential component in actor-critic based deep reinforcement learning (RL). Conventionally, these functions are trained as a regression task by minimising th…
Self-Supervised Theorem Discovery in a Formal Axiomatic System
Kazuki Ota, Takayuki Osa, Tatsuya Harada
Recent artificial intelligence (AI) systems have shown remarkable progress in mathematical reasoning. Many existing approaches, including large language models (LLMs), draw on huma…
Rethinking Policy Diversity in Ensemble Policy Gradient in Large-Scale Reinforcement Learning
Naoki Shitanda, Motoki Omura, Tatsuya Harada +1
Scaling reinforcement learning to tens of thousands of parallel environments requires overcoming the limited exploration capacity of a single policy. Ensemble-based policy gradient…
Cross-Embodiment Offline Reinforcement Learning for Heterogeneous Robot Datasets
Haruki Abe, Takayuki Osa, Yusuke Mukuta +1
Scalable robot policy pre-training has been hindered by the high cost of collecting high-quality demonstrations for each platform. In this study, we address this issue by uniting o…
Revisiting Regularized Policy Optimization for Stable and Efficient Reinforcement Learning in Two-Player Games
Kazuki Ota, Takayuki Osa, Motoki Omura +1
Two-player games such as board games have long been used as traditional benchmarks for reinforcement learning. This work revisits a policy optimization method with reverse Kullback…
Offline Reinforcement Learning with Wasserstein Regularization via Optimal Transport Maps
Motoki Omura, Yusuke Mukuta, Kazuki Ota +2
Offline reinforcement learning (RL) aims to learn an optimal policy from a static dataset, making it particularly valuable in scenarios where data collection is costly, such as rob…
Gradual Transition from Bellman Optimality Operator to Bellman Operator in Online Reinforcement Learning
Motoki Omura, Kazuki Ota, Takayuki Osa +2
For continuous action spaces, actor-critic methods are widely used in online reinforcement learning (RL). However, unlike RL algorithms for discrete actions, which generally model…
Towards Embodiment Scaling Laws in Robot Locomotion
Bo Ai, Liu Dai, Nico Bohlinger +7
Cross-embodiment generalization underpins the vision of building generalist embodied agents for any robot, yet its enabling factors remain poorly understood. We investigate embodim…
RLDG: Robotic Generalist Policy Distillation via Reinforcement Learning
Charles Xu, Qiyang Li, Jianlan Luo +1
Recent advances in robotic foundation models have enabled the development of generalist policies that can adapt to diverse tasks. While these models show impressive flexibility, th…
Discovering Multiple Solutions from a Single Task in Offline Reinforcement Learning
Takayuki Osa, Tatsuya Harada
Recent studies on online reinforcement learning (RL) have demonstrated the advantages of learning multiple behaviors from a single task, as in the case of few-shot adaptation to a…
Offline Reinforcement Learning from Datasets with Structured Non-Stationarity
Johannes Ackermann, Takayuki Osa, Masashi Sugiyama
Current Reinforcement Learning (RL) is often limited by the large amount of data needed to learn a successful policy. Offline RL aims to solve this issue by using transitions colle…
Robustifying a Policy in Multi-Agent RL with Diverse Cooperative Behaviors and Adversarial Style Sampling for Assistive Tasks
Takayuki Osa, Tatsuya Harada
Autonomous assistance of people with motor impairments is one of the most promising applications of autonomous robotic systems. Recent studies have reported encouraging results usi…
Symmetric Q-learning: Reducing Skewness of Bellman Error in Online Reinforcement Learning
Motoki Omura, Takayuki Osa, Yusuke Mukuta +1
In deep reinforcement learning, estimating the value function to evaluate the quality of states and actions is essential. The value function is often trained using the least square…
Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Embodiment Collaboration, Abby O'Neill, Abdul Rehman +291
Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, thi…