Prioritized Experience Replay DQN
E98475
Prioritized Experience Replay DQN is a variant of the Deep Q-Network algorithm that improves learning efficiency by sampling more informative experiences with higher priority from the replay buffer.
All labels observed (3)
| Label | Occurrences |
|---|---|
| Prioritized Experience Replay | 3 |
| Prioritized DQN | 1 |
| Prioritized Experience Replay DQN canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T824085 — 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: Prioritized Experience Replay DQN Context triple: [OpenAI Baselines, implementsAlgorithm, Prioritized Experience Replay DQN]
-
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.
OpenAI Baselines
OpenAI Baselines is a collection of high-quality reference implementations of reinforcement learning algorithms released by OpenAI for research and benchmarking.
-
D.
DRL
DRL is the U.S. State Department bureau responsible for promoting democracy, protecting human rights, and advancing labor rights worldwide.
-
E.
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.
- 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: Prioritized Experience Replay DQN Target entity description: Prioritized Experience Replay DQN is a variant of the Deep Q-Network algorithm that improves learning efficiency by sampling more informative experiences with higher priority from the replay buffer.
-
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.
OpenAI Baselines
OpenAI Baselines is a collection of high-quality reference implementations of reinforcement learning algorithms released by OpenAI for research and benchmarking.
-
D.
DRL
DRL is the U.S. State Department bureau responsible for promoting democracy, protecting human rights, and advancing labor rights worldwide.
-
E.
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.
- F. None of above. chosen
Statements (48)
| Predicate | Object |
|---|---|
| instanceOf |
Deep Q-Network variant
ⓘ
deep reinforcement learning algorithm ⓘ |
| addresses |
inefficiency of uniform experience replay
ⓘ
learning from many uninformative transitions ⓘ |
| aimsTo |
improve learning efficiency
ⓘ
improve sample efficiency ⓘ speed up convergence ⓘ |
| applicationDomain |
Atari game playing
ⓘ
control tasks ⓘ |
| basedOn |
Atari deep Q-network
ⓘ
surface form:
Deep Q-Network
|
| benefit |
can improve performance on Atari 2600 benchmarks
ⓘ
focuses updates on transitions with high learning potential ⓘ |
| category | value-based deep reinforcement learning ⓘ |
| compatibleWith |
Double DQN
ⓘ
Dueling DQN ⓘ other off-policy value-based methods ⓘ |
| coreIdea |
prioritize transitions with large temporal-difference error
ⓘ
sample more informative transitions with higher probability ⓘ |
| evaluation | outperforms baseline DQN with uniform replay on many games ⓘ |
| extends | uniform experience replay ⓘ |
| field | reinforcement learning ⓘ |
| hasComponent |
importance sampling weight computation
ⓘ
priority update mechanism ⓘ priority-based sampling mechanism ⓘ |
| hyperparameter |
alpha controls degree of prioritization
ⓘ
beta controls strength of importance sampling correction ⓘ |
| influenced | later prioritized replay methods in RL ⓘ |
| introducedInPaper |
Prioritized Experience Replay DQN
self-linksurface differs
ⓘ
surface form:
Prioritized Experience Replay
|
| learningSignal | temporal-difference error magnitude ⓘ |
| modifies | sampling distribution over replay buffer ⓘ |
| proposedBy |
David Silver
ⓘ
Ioannis Antonoglou ⓘ John Quan ⓘ Tom Schaul ⓘ |
| publishedAt |
ICLR
ⓘ
surface form:
International Conference on Learning Representations 2016
|
| requires |
correction of sampling bias via importance sampling
ⓘ
storage of priorities alongside transitions in replay buffer ⓘ |
| samplingStrategy |
proportional prioritization
ⓘ
rank-based prioritization ⓘ |
| tradeOff | focus on rare high-error transitions vs coverage of state space ⓘ |
| trainingType | off-policy learning ⓘ |
| uses |
experience replay buffer
ⓘ
importance sampling exponent hyperparameter beta ⓘ importance sampling weights ⓘ neural network function approximator ⓘ priority exponent hyperparameter alpha ⓘ stochastic sampling from replay buffer ⓘ temporal-difference error as priority signal ⓘ |
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: Prioritized Experience Replay DQN Description of subject: Prioritized Experience Replay DQN is a variant of the Deep Q-Network algorithm that improves learning efficiency by sampling more informative experiences with higher priority from the replay buffer.
Referenced by (5)
Full triples — surface form annotated when it differs from this entity's canonical label.
this entity surface form:
Prioritized Experience Replay
this entity surface form:
Prioritized Experience Replay
Prioritized Experience Replay DQN
→
introducedInPaper
→
Prioritized Experience Replay DQN
self-linksurface differs
ⓘ
this entity surface form:
Prioritized Experience Replay
this entity surface form:
Prioritized DQN