Continuous Control
State-of-the-art leaderboards
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Greatest papers with code
Near-Optimal Representation Learning for Hierarchical Reinforcement Learning
ICLR 2019
• tensorflow/models
• 
We study the problem of representation learning in goal-conditioned hierarchical reinforcement learning.
CONTINUOUS CONTROL HIERARCHICAL REINFORCEMENT LEARNING REPRESENTATION LEARNING
Parameter Space Noise for Exploration
ICLR 2018
• tensorflow/models
•
Combining parameter noise with traditional RL methods allows to combine the best of both worlds.
Benchmarking Deep Reinforcement Learning for Continuous Control
• rll/rllab
•
Recently, researchers have made significant progress combining the advances in deep learning for learning feature representations with reinforcement learning.
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
ICML 2018
• facebookresearch/Horizon
• 
A platform for Applied Reinforcement Learning (Applied RL)
Continuous control with deep reinforcement learning
We adapt the ideas underlying the success of Deep Q-Learning to the continuous action domain.
DeepMind Control Suite
The DeepMind Control Suite is a set of continuous control tasks with a standardised structure and interpretable rewards, intended to serve as performance benchmarks for reinforcement learning agents.
ACE: An Actor Ensemble Algorithm for Continuous Control with Tree Search
In this paper, we propose an actor ensemble algorithm, named ACE, for continuous control with a deterministic policy in reinforcement learning.
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.
Sample Efficient Actor-Critic with Experience Replay
This paper presents an actor-critic deep reinforcement learning agent with experience replay that is stable, sample efficient, and performs remarkably well on challenging environments, including the discrete 57-game Atari domain and several continuous control problems.
Learning Latent Dynamics for Planning from Pixels
Planning has been very successful for control tasks with known environment dynamics.