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Found 8 papers, 4 papers with code
When is Offline Policy Selection Sample Efficient for Reinforcement Learning?
As a result, no OPS method can be more sample efficient than OPE in the worst case.
Distributed Reinforcement Learning of Targeted Grasping with Active Vision for Mobile Manipulators
Developing personal robots that can perform a diverse range of manipulation tasks in unstructured environments necessitates solving several challenges for robotic grasping systems.
Periodic Intra-Ensemble Knowledge Distillation for Reinforcement Learning
PIEKD is a learning framework that uses an ensemble of policies to act in the environment while periodically sharing knowledge amongst policies in the ensemble through knowledge distillation.
ChainerRL: A Deep Reinforcement Learning Library
In this paper, we introduce ChainerRL, an open-source deep reinforcement learning (DRL) library built using Python and the Chainer deep learning framework.
Learning Latent State Spaces for Planning through Reward Prediction
The latent representation is learned exclusively from multi-step reward prediction which we show to be the only necessary information for successful planning.
Model-based Reinforcement Learning
reinforcement-learning
+1
Collaborative Inter-agent Knowledge Distillation for Reinforcement Learning
Reinforcement Learning (RL) has demonstrated promising results across several sequential decision-making tasks.
Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations
A critical flaw of existing inverse reinforcement learning (IRL) methods is their inability to significantly outperform the demonstrator.
Deterministic Implementations for Reproducibility in Deep Reinforcement Learning
One by one, we then allow individual sources of nondeterminism to affect our otherwise deterministic implementation, and measure the impact of each source on the variance in performance.
