Hindsight Experience Replay
E98482
Hindsight Experience Replay is a reinforcement learning technique that improves sample efficiency by reinterpreting failed attempts as successful experiences toward alternative goals.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Hindsight Experience Replay canonical | 2 |
How this entity was disambiguated
This entity first appeared as the object of triple T824096 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hindsight Experience Replay Context triple: [OpenAI Baselines, implementsAlgorithm, Hindsight Experience Replay]
-
A.
Atari deep Q-network
The Atari deep Q-network is a pioneering deep reinforcement learning system that learned to play a wide range of Atari 2600 video games directly from raw pixels at human-level or better performance.
-
B.
Arcade Learning Environment
Arcade Learning Environment is a widely used research platform that provides a suite of Atari 2600 games for developing and evaluating reinforcement learning algorithms.
-
C.
MuZero
MuZero is a DeepMind reinforcement learning algorithm that learns to plan and master complex games like Go, chess, and Atari without being given the rules in advance.
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D.
OpenAI Baselines
OpenAI Baselines is a collection of high-quality reference implementations of reinforcement learning algorithms released by OpenAI for research and benchmarking.
-
E.
DRL
DRL is the U.S. State Department bureau responsible for promoting democracy, protecting human rights, and advancing labor rights worldwide.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hindsight Experience Replay Target entity description: Hindsight Experience Replay is a reinforcement learning technique that improves sample efficiency by reinterpreting failed attempts as successful experiences toward alternative goals.
-
A.
Atari deep Q-network
The Atari deep Q-network is a pioneering deep reinforcement learning system that learned to play a wide range of Atari 2600 video games directly from raw pixels at human-level or better performance.
-
B.
Arcade Learning Environment
Arcade Learning Environment is a widely used research platform that provides a suite of Atari 2600 games for developing and evaluating reinforcement learning algorithms.
-
C.
MuZero
MuZero is a DeepMind reinforcement learning algorithm that learns to plan and master complex games like Go, chess, and Atari without being given the rules in advance.
-
D.
OpenAI Baselines
OpenAI Baselines is a collection of high-quality reference implementations of reinforcement learning algorithms released by OpenAI for research and benchmarking.
-
E.
DRL
DRL is the U.S. State Department bureau responsible for promoting democracy, protecting human rights, and advancing labor rights worldwide.
- F. None of above. chosen
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 ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Hindsight Experience Replay Description of subject: Hindsight Experience Replay is a reinforcement learning technique that improves sample efficiency by reinterpreting failed attempts as successful experiences toward alternative goals.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.