A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control | Paper Citation Record

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This paper cites G., Martinez-Canabal, A., Restivo, L., Yiu, A.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control G., Martinez-Canabal, A., Restivo, L., Yiu, A

Reference 1

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A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Unresolved cited work

Reference 2

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This paper cites Hindsight Experience Replay.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Hindsight Experience Replay

Reference 3

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This paper cites Towards Deeper Deep Reinforcement Learning with Spectral Normalization.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Towards Deeper Deep Reinforcement Learning with Spectral Normalization

Reference 4

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This paper cites Randomized Ensembled Double Q-Learning: Learning Fast Without a Model.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Randomized Ensembled Double Q-Learning: Learning Fast Without a Model

Reference 5

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This paper cites Reinforcement Learning with Combinatorial Actions: An Application to Vehicle Routing.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Reinforcement Learning with Combinatorial Actions: An Application to Vehicle Routing

Reference 6

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Observation 87541af9-5a2f-49b7-b6b7-f11c5d8562f6 · outbound

This paper cites G., and Courville, A.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control G., and Courville, A

Reference 7

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This paper cites Revisiting fundamentals of experience replay.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Revisiting fundamentals of experience replay

Reference 8

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

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Addressing function approximation error in actor-critic methods

Reference 9

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This paper cites Off-Policy Deep Reinforcement Learning without Exploration.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Off-Policy Deep Reinforcement Learning without Exploration

Reference 10

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This paper cites Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

Reference 11

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This paper cites Mastering Diverse Domains through World Models.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Mastering Diverse Domains through World Models

Reference 12

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This paper cites On the role of planning in model-based deep reinforcement learning.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control On the role of planning in model-based deep reinforcement learning

Reference 13

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This paper cites TD-MPC2: Scalable, Robust World Models for Continuous Control.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control TD-MPC2: Scalable, Robust World Models for Continuous Control

Reference 14

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This paper cites Deep Learning Scaling is Predictable, Empirically.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Deep Learning Scaling is Predictable, Empirically

Reference 15

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A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Unresolved cited work

Reference 16

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This paper cites Offline Q-Learning on Diverse Multi-Task Data Both Scales And Generalizes.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Offline Q-Learning on Diverse Multi-Task Data Both Scales And Generalizes

Reference 17

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Observation e7cfef08-df5d-406c-a6cb-6f94a450dca7 · outbound

This paper cites Maintaining Plasticity in Continual Learning via Regenerative Regularization.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Maintaining Plasticity in Continual Learning via Regenerative Regularization

Reference 18

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This paper cites Maxmin Q-learning: Controlling the Estimation Bias of Q-learning.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Maxmin Q-learning: Controlling the Estimation Bias of Q-learning

Reference 19

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This paper cites Crafting papers on machine learning.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Crafting papers on machine learning

Reference 20

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This paper cites SimBa: Simplicity Bias for Scaling Up Parameters in Deep Reinforcement Learning.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control SimBa: Simplicity Bias for Scaling Up Parameters in Deep Reinforcement Learning

Reference 21

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This paper cites Multi-Game Decision Transformers.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Multi-Game Decision Transformers

Reference 22

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This paper cites Efficient Deep Reinforcement Learning Requires Regulating Overfitting.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Efficient Deep Reinforcement Learning Requires Regulating Overfitting

Reference 23

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This paper cites Self-improving reactive agents based on reinforcement learning, planning and teaching.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Self-improving reactive agents based on reinforcement learning, planning and teaching

Reference 24

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This paper cites Neuroplastic Expansion in Deep Reinforcement Learning.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Neuroplastic Expansion in Deep Reinforcement Learning

Reference 25

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This paper cites Offline-Boosted Actor-Critic: Adaptively Blending Optimal Historical Behaviors in Deep Off-Policy RL.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Offline-Boosted Actor-Critic: Adaptively Blending Optimal Historical Behaviors in Deep Off-Policy RL

Reference 26

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This paper cites Off-Policy RL Algorithms Can be Sample-Efficient for Continuous Control via Sample Multiple Reuse.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Off-Policy RL Algorithms Can be Sample-Efficient for Continuous Control via Sample Multiple Reuse

Reference 27

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This paper cites Revisiting plasticity in visual reinforcement learning: Data.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Revisiting plasticity in visual reinforcement learning: Data

Reference 28

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This paper cites Learning better with less: effective augmentation for sample-efficient visual reinforcement learning.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Learning better with less: effective augmentation for sample-efficient visual reinforcement learning

Reference 29

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This paper cites A., Veness, J., Bellemare, M.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control A., Veness, J., Bellemare, M

Reference 30

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This paper cites Tactical Optimism and Pessimism for Deep Reinforcement Learning.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Tactical Optimism and Pessimism for Deep Reinforcement Learning

Reference 31

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This paper cites Safe and efficient off-policy reinforcement learning.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Safe and efficient off-policy reinforcement learning

Reference 32

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This paper cites Overestimation, Overfitting, and Plasticity in Actor-Critic: the Bitter Lesson of Reinforcement Learning.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Overestimation, Overfitting, and Plasticity in Actor-Critic: the Bitter Lesson of Reinforcement Learning

Reference 33

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This paper cites Bigger, Regularized, Optimistic: scaling for compute and sample-efficient continuous control.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Bigger, Regularized, Optimistic: scaling for compute and sample-efficient continuous control

Reference 34

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A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control The primacy bias in deep reinforcement learning

Reference 35

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This paper cites The primacy bias in deep reinforcement learning.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control The primacy bias in deep reinforcement learning

Reference 36

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This paper cites Deep Reinforcement Learning with Plasticity Injection.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Deep Reinforcement Learning with Plasticity Injection

Reference 37

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This paper cites F., Maximo, M.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control F., Maximo, M

Reference 38

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This paper cites Mind the Model, Not the Agent: The Primacy Bias in Model-based RL.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Mind the Model, Not the Agent: The Primacy Bias in Model-based RL

Reference 39

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This paper cites Prioritized Experience Replay.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Prioritized Experience Replay

Reference 40

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This paper cites Bigger, Better, Faster: Human-level Atari with human-level efficiency.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Bigger, Better, Faster: Human-level Atari with human-level efficiency

Reference 41

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This paper cites HumanoidBench: Simulated Humanoid Benchmark for Whole-Body Locomotion and Manipulation.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control HumanoidBench: Simulated Humanoid Benchmark for Whole-Body Locomotion and Manipulation

Reference 42

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This paper cites D2RL: Deep Dense Architectures in Reinforcement Learning.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control D2RL: Deep Dense Architectures in Reinforcement Learning

Reference 43

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This paper cites S., and Evci, U.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control S., and Evci, U

Reference 44

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This paper cites Model-based off-policy deep reinforcement learning with model-embedding.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Model-based off-policy deep reinforcement learning with model-embedding

Reference 45

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A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control DeepMind Control Suite

Reference 46

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A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Mujoco: A physics engine for model-based control

Reference 47

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A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Deep Reinforcement Learning and the Deadly Triad

Reference 48

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This paper cites DrM: Mastering Visual Reinforcement Learning through Dormant Ratio Minimization.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control DrM: Mastering Visual Reinforcement Learning through Dormant Ratio Minimization

Reference 49

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A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Unresolved cited work

Reference 50

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This paper cites CUER: Corrected Uniform Experience Replay for Off-Policy Continuous Deep Reinforcement Learning Algorithms.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control CUER: Corrected Uniform Experience Replay for Off-Policy Continuous Deep Reinforcement Learning Algorithms

Reference 51

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This paper cites Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning

Reference 52

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This paper cites A Deeper Look at Experience Replay.

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control A Deeper Look at Experience Replay

Reference 53

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A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control write newline

Reference 54

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