Hindsight Experience Replay

E98482

experience replay method reinforcement learning technique

Hindsight Experience Replay is a reinforcement learning technique that improves sample efficiency by reinterpreting failed attempts as successful experiences toward alternative goals.

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All labels observed (1)

Label	Occurrences
Hindsight Experience Replay canonical	2

Statements (45)

Predicate	Object
instanceOf	experience replay method ⓘ reinforcement learning technique ⓘ
abbreviation	HER ⓘ
aimsTo	enable learning from sparse rewards ⓘ improve sample efficiency ⓘ reuse failed trajectories as successful ones for alternative goals ⓘ
appliedTo	multi-goal environments ⓘ robotic manipulation tasks ⓘ sparse reward environments ⓘ
assumes	goals can be derived from achieved states ⓘ
category	off-policy data augmentation technique ⓘ
citationCountCategory	highly cited reinforcement learning method ⓘ
compatibleWith	DDPG ⓘ surface form: Deep Deterministic Policy Gradient Deep Q-Learning ⓘ actor-critic methods ⓘ
coreIdea	reinterpret failed attempts as successful experiences toward different goals ⓘ
field	machine learning ⓘ reinforcement learning ⓘ
implementedIn	OpenAI Baselines ⓘ Stable Baselines ⓘ
improves	data efficiency of reinforcement learning agents ⓘ learning speed in sparse reward settings ⓘ
influenced	Goal-Conditioned HER variants ⓘ Hindsight Policy Gradients ⓘ multi-goal RL benchmarks such as Fetch environments ⓘ
introducedInPaper	Hindsight Experience Replay self-link ⓘ
keyMechanism	relabelling goals in stored trajectories ⓘ
modifies	replay buffer sampling strategy ⓘ
operatesOn	goal-conditioned policies ⓘ
proposedBy	Alex Ray ⓘ Bob McGrew ⓘ Filip Wolski ⓘ Jonas Schneider ⓘ Josh Tobin ⓘ Marcin Andrychowicz ⓘ OpenAI researchers ⓘ Peter Welinder ⓘ Rachel Fong ⓘ
publicationYear	2017 ⓘ
publishedAtConference	NeurIPS ⓘ surface form: NeurIPS 2017
relatedTo	Universal Value Function Approximators ⓘ goal-conditioned reinforcement learning ⓘ
requires	goal representation in state space ⓘ
uses	experience replay buffer ⓘ off-policy reinforcement learning ⓘ

Referenced by (2)

Full triples — surface form annotated when it differs from this entity's canonical label.

OpenAI Baselines → implementsAlgorithm → Hindsight Experience Replay ⓘ

Hindsight Experience Replay → introducedInPaper → Hindsight Experience Replay self-link ⓘ