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Found 6 papers, 3 papers with code
Identifying Policy Gradient Subspaces
Policy gradient methods hold great potential for solving complex continuous control tasks.
MIMo: A Multi-Modal Infant Model for Studying Cognitive Development
Human intelligence and human consciousness emerge gradually during the process of cognitive development.
Investigating the Impact of Action Representations in Policy Gradient Algorithms
Reinforcement learning~(RL) is a versatile framework for learning to solve complex real-world tasks.
Natural and Robust Walking using Reinforcement Learning without Demonstrations in High-Dimensional Musculoskeletal Models
Humans excel at robust bipedal walking in complex natural environments.
Learning with Muscles: Benefits for Data-Efficiency and Robustness in Anthropomorphic Tasks
Humans are able to outperform robots in terms of robustness, versatility, and learning of new tasks in a wide variety of movements.
DEP-RL: Embodied Exploration for Reinforcement Learning in Overactuated and Musculoskeletal Systems
Muscle-actuated organisms are capable of learning an unparalleled diversity of dexterous movements despite their vast amount of muscles.
