Balancing Plasticity and Stability with Fast and Slow Successor Features | Paper Citation Record

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Balancing Plasticity and Stability with Fast and Slow Successor Features Unresolved cited work

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This paper cites P., and Singh, S.

Balancing Plasticity and Stability with Fast and Slow Successor Features P., and Singh, S

Reference 2

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Balancing Plasticity and Stability with Fast and Slow Successor Features and Precup, D

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Balancing Plasticity and Stability with Fast and Slow Successor Features J., Schaul, T., van Hasselt, H

Reference 4

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This paper cites G., Naddaf, Y., Veness, J., and Bowling, M.

Balancing Plasticity and Stability with Fast and Slow Successor Features G., Naddaf, Y., Veness, J., and Bowling, M

Reference 5

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This paper cites Lifelong Reinforcement Learning with Modulating Masks.

Balancing Plasticity and Stability with Fast and Slow Successor Features Lifelong Reinforcement Learning with Modulating Masks

Reference 6

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Balancing Plasticity and Stability with Fast and Slow Successor Features Unresolved cited work

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This paper cites Experiment tracking with weights and biases, 2020.

Balancing Plasticity and Stability with Fast and Slow Successor Features Experiment tracking with weights and biases, 2020

Reference 8

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This paper cites Universal Successor Features Approximators.

Balancing Plasticity and Stability with Fast and Slow Successor Features Universal Successor Features Approximators

Reference 9

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This paper cites J., Leary, C., Maclaurin, D., Necula, G., Paszke, A., Vander P las, J., Wanderman- M ilne, S., and Zhang, Q.

Balancing Plasticity and Stability with Fast and Slow Successor Features J., Leary, C., Maclaurin, D., Necula, G., Paszke, A., Vander P las, J., Wanderman- M ilne, S., and Zhang, Q

Reference 10

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This paper cites Task-agnostic continual reinforcement learning: Gaining insights and overcoming challenges.

Balancing Plasticity and Stability with Fast and Slow Successor Features Task-agnostic continual reinforcement learning: Gaining insights and overcoming challenges

Reference 11

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This paper cites Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks.

Balancing Plasticity and Stability with Fast and Slow Successor Features Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks

Reference 12

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This paper cites A., and Precup, D.

Balancing Plasticity and Stability with Fast and Slow Successor Features A., and Precup, D

Reference 13

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Balancing Plasticity and Stability with Fast and Slow Successor Features F., Lan, Q., Rahman, P., Mahmood, A

Reference 14

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This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Balancing Plasticity and Stability with Fast and Slow Successor Features An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 15

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Balancing Plasticity and Stability with Fast and Slow Successor Features Unresolved cited work

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This paper cites Addressing function approximation error in actor-critic methods, 2018.

Balancing Plasticity and Stability with Fast and Slow Successor Features Addressing function approximation error in actor-critic methods, 2018

Reference 17

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This paper cites J raph: A library for graph neural networks in jax., 2020.

Balancing Plasticity and Stability with Fast and Slow Successor Features J raph: A library for graph neural networks in jax., 2020

Reference 18

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This paper cites F lax: A neural network library and ecosystem for JAX , 2024.

Balancing Plasticity and Stability with Fast and Slow Successor Features F lax: A neural network library and ecosystem for JAX , 2024

Reference 19

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This paper cites D., 2007, @doi [Computing in Science and Engineering] 10.1109/MCSE.2007.55 , 9, 90.

Balancing Plasticity and Stability with Fast and Slow Successor Features D., 2007, @doi [Computing in Science and Engineering] 10.1109/MCSE.2007.55 , 9, 90

Reference 20

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This paper cites Continual reinforcement learning with complex synapses.

Balancing Plasticity and Stability with Fast and Slow Successor Features Continual reinforcement learning with complex synapses

Reference 21

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Balancing Plasticity and Stability with Fast and Slow Successor Features Policy Consolidation for Continual Reinforcement Learning

Reference 22

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This paper cites Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignment.

Balancing Plasticity and Stability with Fast and Slow Successor Features Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignment

Reference 23

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This paper cites Towards continual reinforcement learning: A review and perspectives.

Balancing Plasticity and Stability with Fast and Slow Successor Features Towards continual reinforcement learning: A review and perspectives

Reference 24

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Balancing Plasticity and Stability with Fast and Slow Successor Features Adam: A Method for Stochastic Optimization

Reference 25

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Balancing Plasticity and Stability with Fast and Slow Successor Features Auto-Encoding Variational Bayes

Reference 26

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Balancing Plasticity and Stability with Fast and Slow Successor Features A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al

Reference 27

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Balancing Plasticity and Stability with Fast and Slow Successor Features Jupyter notebooks -- a publishing format for reproducible computational workflows

Reference 28

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Balancing Plasticity and Stability with Fast and Slow Successor Features Slow and Steady Wins the Race: Maintaining Plasticity with Hare and Tortoise Networks

Reference 29

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Balancing Plasticity and Stability with Fast and Slow Successor Features Continuous control with deep reinforcement learning

Reference 30

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Balancing Plasticity and Stability with Fast and Slow Successor Features Disentangling the Causes of Plasticity Loss in Neural Networks

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Balancing Plasticity and Stability with Fast and Slow Successor Features L., McNaughton, B

Reference 32

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Balancing Plasticity and Stability with Fast and Slow Successor Features and Cohen, N

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Balancing Plasticity and Stability with Fast and Slow Successor Features The primacy bias in deep reinforcement learning

Reference 34

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Balancing Plasticity and Stability with Fast and Slow Successor Features Deep reinforcement learning with plasticity injection

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Balancing Plasticity and Stability with Fast and Slow Successor Features Pytorch: An imperative style, high-performance deep learning library

Reference 36

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Balancing Plasticity and Stability with Fast and Slow Successor Features Self-activating neural ensembles for continual reinforcement learning

Reference 37

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Balancing Plasticity and Stability with Fast and Slow Successor Features Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference

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Balancing Plasticity and Stability with Fast and Slow Successor Features Experience replay for continual learning

Reference 39

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Balancing Plasticity and Stability with Fast and Slow Successor Features Proximal Policy Optimization Algorithms

Reference 40

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Balancing Plasticity and Stability with Fast and Slow Successor Features W., Pascanu, R., and Hadsell, R

Reference 41

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Balancing Plasticity and Stability with Fast and Slow Successor Features and Sutton, R

Reference 42

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Balancing Plasticity and Stability with Fast and Slow Successor Features Deterministic policy gradient algorithms

Reference 43

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Balancing Plasticity and Stability with Fast and Slow Successor Features S., and Evci, U

Reference 44

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Balancing Plasticity and Stability with Fast and Slow Successor Features Unresolved cited work

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Balancing Plasticity and Stability with Fast and Slow Successor Features Mujoco: A physics engine for model-based control

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Balancing Plasticity and Stability with Fast and Slow Successor Features dm\_control: Software and tasks for continuous control

Reference 47

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Balancing Plasticity and Stability with Fast and Slow Successor Features Deep reinforcement learning with double q-learning

Reference 48

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Balancing Plasticity and Stability with Fast and Slow Successor Features and Drake, F

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Balancing Plasticity and Stability with Fast and Slow Successor Features doi:10.21105/joss.03021

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Balancing Plasticity and Stability with Fast and Slow Successor Features Deep Reinforcement Learning amidst Lifelong Non-Stationarity

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This paper cites Hydra - a framework for elegantly configuring complex applications.

Balancing Plasticity and Stability with Fast and Slow Successor Features Hydra - a framework for elegantly configuring complex applications

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This paper cites Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning.

Balancing Plasticity and Stability with Fast and Slow Successor Features Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning

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Balancing Plasticity and Stability with Fast and Slow Successor Features Continual learning through synaptic intelligence

Reference 54

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Balancing Plasticity and Stability with Fast and Slow Successor Features write newline

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