• Corpus ID: 230770431

# Reinforcement Learning with Latent Flow

@inproceedings{Shang2021ReinforcementLW,
title={Reinforcement Learning with Latent Flow},
author={Wenling Shang and Xiaofei Wang and A. Srinivas and Aravind Rajeswaran and Yang Gao and P. Abbeel and Michael Laskin},
booktitle={NeurIPS},
year={2021}
}
• Published in NeurIPS 6 January 2021
• Computer Science
Temporal information is essential to learning effective policies with Reinforcement Learning (RL). However, current state-of-the-art RL algorithms either assume that such information is given as part of the state space or, when learning from pixels, use the simple heuristic of frame-stacking to implicitly capture temporal information present in the image observations. This heuristic is in contrast to the current paradigm in video classification architectures, which utilize explicit encodings of…

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## References

SHOWING 1-10 OF 60 REFERENCES

### Improving Sample Efficiency in Model-Free Reinforcement Learning from Images

• Computer Science
AAAI
• 2021
A simple approach capable of matching state-of-the-art model-free and model-based algorithms on MuJoCo control tasks and demonstrating robustness to observational noise, surpassing existing approaches in this setting.

### Decoupling Representation Learning from Reinforcement Learning

• Computer Science
ICML
• 2021
A new unsupervised learning task, called Augmented Temporal Contrast (ATC), which trains a convolutional encoder to associate pairs of observations separated by a short time difference, under image augmentations and using a contrastive loss.

### Reinforcement Learning with Augmented Data

• Computer Science
NeurIPS
• 2020
It is shown that augmentations such as random translate, crop, color jitter, patch cutout, random convolutions, and amplitude scale can enable simple RL algorithms to outperform complex state-of-the-art methods across common benchmarks.

### Data-Efficient Reinforcement Learning with Momentum Predictive Representations

• Computer Science
ArXiv
• 2020
This work trains an agent to predict its own latent state representations multiple steps into the future using an encoder which is an exponential moving average of the agent's parameters, and makes predictions using a learned transition model.

### Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels

• Computer Science
ICLR
• 2021
The addition of the augmentation method dramatically improves SAC's performance, enabling it to reach state-of-the-art performance on the DeepMind control suite, surpassing model-based methods and recently proposed contrastive learning (CURL).

### Learning Latent Dynamics for Planning from Pixels

• Computer Science
ICML
• 2019
The Deep Planning Network (PlaNet) is proposed, a purely model-based agent that learns the environment dynamics from images and chooses actions through fast online planning in latent space using a latent dynamics model with both deterministic and stochastic transition components.

### Dueling Network Architectures for Deep Reinforcement Learning

• Computer Science
ICML
• 2016
This paper presents a new neural network architecture for model-free reinforcement learning that leads to better policy evaluation in the presence of many similar-valued actions and enables the RL agent to outperform the state-of-the-art on the Atari 2600 domain.

### Reinforcement Learning with Unsupervised Auxiliary Tasks

• Computer Science
ICLR
• 2017
This paper significantly outperforms the previous state-of-the-art on Atari, averaging 880\% expert human performance, and a challenging suite of first-person, three-dimensional \emph{Labyrinth} tasks leading to a mean speedup in learning of 10$\times$ and averaging 87\% Expert human performance on Labyrinth.

### Motion Perception in Reinforcement Learning with Dynamic Objects

• Computer Science
CoRL
• 2018
It is shown that for continuous control tasks learning an explicit representation of motion improves the quality of the learned controller in dynamic scenarios, and that using an image difference between the current and the previous frame as an additional input leads to better results than a temporal stack of frames.

### Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

• Computer Science
ICML
• 2018
This paper proposes soft actor-critic, an off-policy actor-Critic deep RL algorithm based on the maximum entropy reinforcement learning framework, and achieves state-of-the-art performance on a range of continuous control benchmark tasks, outperforming prior on-policy and off- policy methods.