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Research map / Reinforcement learning / Visual and representation RL

Synthetic Experience Replay

NeurIPS 2023 · Reinforcement learning › Visual and representation RL · approach: Probabilistic and autonomous RL · 12 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
replace VAE/GAN generator with diffusionSynthER · halfcheetah-medium-replayTD3+BC return: 27.1 -> 45.9Helped
replace offline data with SynthER synthetic dataTD3+BC · D4RL locomotion averagenormalized return: 59.0 -> 60.0No clear effect
replace offline data with SynthER synthetic dataEDAC · D4RL maze2d averagenormalized return: 82.6 -> 102.9Helped
use larger network with SynthER dataTD3+BC · D4RL locomotion averagenormalized return: 59.5 -> 65.9Helped
use larger network with original dataTD3+BC · D4RL locomotion averagenormalized return: 59.0 -> 59.5No clear effect
use larger networkIQL/EDAC · D4RL locomotionnormalized returnNo clear effect
add SynthER latent dataDrQ+BC · V-D4RL averagenormalized return: 51.7 -> 56.5Helped
add SynthER latent dataBC · V-D4RL averagenormalized return: 49.8 -> 53.2Helped
use only actor features as latent (ablation)SynthER DrQ+BC · V-D4RL cheetah expertreturn: 52.3 -> 43.5Hurt
use only critic features as latent (ablation)SynthER DrQ+BC · V-D4RL cheetah expertreturn: 52.3 -> 16.0Hurt
increase denoiser depth from 2 to 6 (residual MLP) (ablation)SynthER · hopper medium-expertTD3+BC return: 78.5 -> 101.1Helped
mix 50-50 real and synthetic data (ablation)SynthER TD3+BC · D4RL locomotion averagenormalized return: 60.0 -> 59.2No clear effect

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