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Found 12 papers, 1 papers with code
Parallel $Q$-Learning: Scaling Off-policy Reinforcement Learning under Massively Parallel Simulation
This paper presents a Parallel $Q$-Learning (PQL) scheme that outperforms PPO in wall-clock time while maintaining superior sample efficiency of off-policy learning.
Statistical Learning under Heterogeneous Distribution Shift
We show that, when the class $F$ is "simpler" than $G$ (measured, e. g., in terms of its metric entropy), our predictor is more resilient to heterogeneous covariate shifts} in which the shift in $\mathbf{x}$ is much greater than that in $\mathbf{y}$.
Is Conditional Generative Modeling all you need for Decision-Making?
We further demonstrate the advantages of modeling policies as conditional diffusion models by considering two other conditioning variables: constraints and skills.
Distributionally Adaptive Meta Reinforcement Learning
In this work, we develop a framework for meta-RL algorithms that are able to behave appropriately under test-time distribution shifts in the space of tasks.
Offline RL Policies Should be Trained to be Adaptive
Offline RL algorithms must account for the fact that the dataset they are provided may leave many facets of the environment unknown.
Overcoming the Spectral Bias of Neural Value Approximation
Value approximation using deep neural networks is at the heart of off-policy deep reinforcement learning, and is often the primary module that provides learning signals to the rest of the algorithm.
Understanding the Generalization Gap in Visual Reinforcement Learning
Deep Reinforcement Learning (RL) agents have achieved superhuman performance on several video game suites.
OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning
Reinforcement learning (RL) has achieved impressive performance in a variety of online settings in which an agent's ability to query the environment for transitions and rewards is effectively unlimited.
Combining Physical Simulators and Object-Based Networks for Control
Physics engines play an important role in robot planning and control; however, many real-world control problems involve complex contact dynamics that cannot be characterized analytically.
Augmenting Physical Simulators with Stochastic Neural Networks: Case Study of Planar Pushing and Bouncing
An efficient, generalizable physical simulator with universal uncertainty estimates has wide applications in robot state estimation, planning, and control.
Reset-Free Guided Policy Search: Efficient Deep Reinforcement Learning with Stochastic Initial States
Autonomous learning of robotic skills can allow general-purpose robots to learn wide behavioral repertoires without requiring extensive manual engineering.
Backprop KF: Learning Discriminative Deterministic State Estimators
We show that this procedure can be used to train state estimators that use complex input, such as raw camera images, which must be processed using expressive nonlinear function approximators such as convolutional neural networks.
