Prioritized Experience Replay DQN

E98475

Deep Q-Network variant deep reinforcement learning algorithm

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.

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All labels observed (5)

Label	Occurrences
Prioritized Experience Replay	5
Distributed Prioritized Experience Replay	1
Prioritized DQN	1
Prioritized Experience Replay DQN canonical	1
Prioritized Experience Replay in Deep Q-Networks	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	Deep Q-Network ⓘ linked to: Atari 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 ⓘ linked to: Prioritized Experience Replay DQN
learningSignal	temporal-difference error magnitude ⓘ
modifies	sampling distribution over replay buffer ⓘ
proposedBy	David Silver ⓘ Ioannis Antonoglou ⓘ John Quan ⓘ Tom Schaul ⓘ
publishedAt	International Conference on Learning Representations 2016 ⓘ linked to: ICLR
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