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Greatest papers with code

Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion

NeurIPS 2018 tensorflow/models

Integrating model-free and model-based approaches in reinforcement learning has the potential to achieve the high performance of model-free algorithms with low sample complexity.

CONTINUOUS CONTROL

Trust-PCL: An Off-Policy Trust Region Method for Continuous Control

ICLR 2018 tensorflow/models

When evaluated on a number of continuous control tasks, Trust-PCL improves the solution quality and sample efficiency of TRPO.

CONTINUOUS CONTROL

Parameter Space Noise for Exploration

ICLR 2018 tensorflow/models

Combining parameter noise with traditional RL methods allows to combine the best of both worlds.

CONTINUOUS CONTROL

Primal Wasserstein Imitation Learning

ICLR 2021 google-research/google-research

Imitation Learning (IL) methods seek to match the behavior of an agent with that of an expert.

CONTINUOUS CONTROL IMITATION LEARNING

Behavior Regularized Offline Reinforcement Learning

26 Nov 2019google-research/google-research

In reinforcement learning (RL) research, it is common to assume access to direct online interactions with the environment.

CONTINUOUS CONTROL OFFLINE RL

Unsupervised Learning of Object Structure and Dynamics from Videos

NeurIPS 2019 google-research/google-research

Extracting and predicting object structure and dynamics from videos without supervision is a major challenge in machine learning.

ACTION RECOGNITION CONTINUOUS CONTROL OBJECT TRACKING VIDEO PREDICTION

Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning

1 Oct 2019google/trax

In this paper, we aim to develop a simple and scalable reinforcement learning algorithm that uses standard supervised learning methods as subroutines.

CONTINUOUS CONTROL OPENAI GYM

Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation

NeurIPS 2017 hill-a/stable-baselines

In this work, we propose to apply trust region optimization to deep reinforcement learning using a recently proposed Kronecker-factored approximation to the curvature.

ATARI GAMES CONTINUOUS CONTROL