dm_control: Software and Tasks for Continuous Control
원문 초록
The dm_control software package is a collection of Python libraries and task suites for reinforcement learning agents in an articulated-body simulation. A MuJoCo wrapper provides convenient bindings to functions and data structures. The PyMJCF and Composer libraries enable procedural model manipulation and task authoring. The Control Suite is a fixed set of tasks with standardised structure, intended to serve as performance benchmarks. The Locomotion framework provides high-level abstractions and examples of locomotion tasks. A set of configurable manipulation tasks with a robot arm and snap-together bricks is also included. dm_control is publicly available at https://www.github.com/deepmind/dm_control
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Sparse-Reg: Improving Sample Complexity in Offline Reinforcement Learning using Sparsity
DR-SAC: Distributionally Robust Soft Actor-Critic for Reinforcement Learning under Uncertainty
CO-PILOT: COllaborative Planning and reInforcement Learning On sub-Task curriculum
Deep Reinforcement Learning with Gradient Eligibility Traces
Supervised Fine Tuning on Curated Data is Reinforcement Learning (and can be improved)
Aligning Humans and Robots via Reinforcement Learning from Implicit Human Feedback
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