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Found 8 papers, 1 papers with code
Real-World Fluid Directed Rigid Body Control via Deep Reinforcement Learning
Recent advances in real-world applications of reinforcement learning (RL) have relied on the ability to accurately simulate systems at scale.
Adversarial Model for Offline Reinforcement Learning
We propose a novel model-based offline Reinforcement Learning (RL) framework, called Adversarial Model for Offline Reinforcement Learning (ARMOR), which can robustly learn policies to improve upon an arbitrary reference policy regardless of data coverage.
ARMOR: A Model-based Framework for Improving Arbitrary Baseline Policies with Offline Data
We propose a new model-based offline RL framework, called Adversarial Models for Offline Reinforcement Learning (ARMOR), which can robustly learn policies to improve upon an arbitrary baseline policy regardless of data coverage.
Leveraging Experience in Lazy Search
If new search problems are sufficiently similar to problems solved during training, the learned policy will choose a good edge evaluation ordering and solve the motion planning problem quickly.
Blending MPC & Value Function Approximation for Efficient Reinforcement Learning
We further propose an algorithm that changes $\lambda$ over time to reduce the dependence on MPC as our estimates of the value function improve, and test the efficacy our approach on challenging high-dimensional manipulation tasks with biased models in simulation.
Information Theoretic Model Predictive Q-Learning
Model-free Reinforcement Learning (RL) works well when experience can be collected cheaply and model-based RL is effective when system dynamics can be modeled accurately.
Leveraging Experience in Lazy Search
If new search problems are sufficiently similar to problems solved during training, the learned policy will choose a good edge evaluation ordering and solve the motion planning problem quickly.
Learning Heuristic Search via Imitation
In this paper, we do so by training a heuristic policy that maps the partial information from the search to decide which node of the search tree to expand.
