## Prioritized Experience Replay

Introduced by Schaul et al. in Prioritized Experience Replay

Prioritized Experience Replay is a type of experience replay in reinforcement learning where we In more frequently replay transitions with high expected learning progress, as measured by the magnitude of their temporal-difference (TD) error. This prioritization can lead to a loss of diversity, which is alleviated with stochastic prioritization, and introduce bias, which can be corrected with importance sampling.

The stochastic sampling method interpolates between pure greedy prioritization and uniform random sampling. The probability of being sampled is ensured to be monotonic in a transition's priority, while guaranteeing a non-zero probability even for the lowest-priority transition. Concretely, define the probability of sampling transition $i$ as

$$P(i) = \frac{p_i^{\alpha}}{\sum_k p_k^{\alpha}}$$

where $p_i > 0$ is the priority of transition $i$. The exponent $\alpha$ determines how much prioritization is used, with $\alpha=0$ corresponding to the uniform case.

Prioritized replay introduces bias because it changes this distribution in an uncontrolled fashion, and therefore changes the solution that the estimates will converge to. We can correct this bias by using importance-sampling (IS) weights:

$$w_{i} = \left(\frac{1}{N}\cdot\frac{1}{P\left(i\right)}\right)^{\beta}$$

that fully compensates for the non-uniform probabilities $P\left(i\right)$ if $\beta = 1$. These weights can be folded into the Q-learning update by using $w_{i}\delta_{i}$ instead of $\delta_{i}$ - weighted IS rather than ordinary IS. For stability reasons, we always normalize weights by $1/\max_{i}w_{i}$ so that they only scale the update downwards.

The two types of prioritization are proportional based, where $p_{i} = |\delta_{i}| + \epsilon$ and rank-based, where $p_{i} = \frac{1}{\text{rank}\left(i\right)}$, the latter where $\text{rank}\left(i\right)$ is the rank of transition $i$ when the replay memory is sorted according to |$\delta_{i}$|, For proportional based, hyperparameters used were $\alpha = 0.7$, $\beta_{0} = 0.5$. For the rank-based variant, hyperparameters used were $\alpha = 0.6$, $\beta_{0} = 0.4$.

#### Latest Papers

PAPER DATE
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The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning
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Firas FredjYasser Al-EryaniSetareh MaghsudiMohamed AkroutEkram Hossain
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Juan ChenZhiwen XiaoHuanlai XingPenglin DaiShouxi LuoMuhammad Azhar Iqbal
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Stacked Auto Encoder Based Deep Reinforcement Learning for Online Resource Scheduling in Large-Scale MEC Networks
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Jin QiuJiangbin LyuLiqun Fu
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| Julian SchrittwieserIoannis AntonoglouThomas HubertKaren SimonyanLaurent SifreSimon SchmittArthur GuezEdward LockhartDemis HassabisThore GraepelTimothy LillicrapDavid Silver
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Deep Reinforcement Learning Based Dynamic Trajectory Control for UAV-assisted Mobile Edge Computing
Liang WangKezhi WangCunhua PanWei XuNauman AslamArumugam Nallanathan
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Task-Oriented Language Grounding for Language Input with Multiple Sub-Goals of Non-Linear Order
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Google Research Football: A Novel Reinforcement Learning Environment
| Karol KurachAnton RaichukPiotr StańczykMichał ZającOlivier BachemLasse EspeholtCarlos RiquelmeDamien VincentMarcin MichalskiOlivier BousquetSylvain Gelly
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Prioritized Guidance for Efficient Multi-Agent Reinforcement Learning Exploration
Qisheng WangQichao Wang
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Prioritized Sequence Experience Replay
Marc BrittainJosh BertramXuxi YangPeng Wei
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Generative Adversarial Imagination for Sample Efficient Deep Reinforcement Learning
Kacper Kielak
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TF-Replicator: Distributed Machine Learning for Researchers
| Peter BuchlovskyDavid BuddenDominik GreweChris JonesJohn AslanidesFrederic BesseAndy BrockAidan ClarkSergio Gómez ColmenarejoAedan PopeFabio ViolaDan Belov
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Macro action selection with deep reinforcement learning in StarCraft
| Sijia XuHongyu KuangZhi ZhuangRenjie HuYang LiuHuyang Sun
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An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution
| Rosanne LiuJoel LehmanPiero MolinoFelipe Petroski SuchEric FrankAlex SergeevJason Yosinski
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Christopher StantonJeff Clune
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Tracy WanNeil Xu
2018-05-15
| Gabriel Barth-MaronMatthew W. HoffmanDavid BuddenWill DabneyDan HorganDhruva TBAlistair MuldalNicolas HeessTimothy Lillicrap
2018-04-23
Distributed Prioritized Experience Replay
| Dan HorganJohn QuanDavid BuddenGabriel Barth-MaronMatteo HesselHado van HasseltDavid Silver
2018-03-02
ScreenerNet: Learning Self-Paced Curriculum for Deep Neural Networks
Tae-Hoon KimJonghyun Choi
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ViZDoom: DRQN with Prioritized Experience Replay, Double-Q Learning, & Snapshot Ensembling
Christopher SchulzeMarcus Schulze
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Rainbow: Combining Improvements in Deep Reinforcement Learning
| Matteo HesselJoseph ModayilHado van HasseltTom SchaulGeorg OstrovskiWill DabneyDan HorganBilal PiotMohammad AzarDavid Silver
2017-10-06
A novel DDPG method with prioritized experience replay
| Yuenan HouLifeng LiuQing WeiXudong XuChunlin Chen
2017-10-01
Prioritized Experience Replay
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Atari Games 5 18.52%
Continuous Control 4 14.81%
Decision Making 2 7.41%
Game of Go 2 7.41%
Efficient Exploration 1 3.70%
Board Games 1 3.70%
Distributional Reinforcement Learning 1 3.70%
Chatbot 1 3.70%
Game of Chess 1 3.70%

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