Continuous Control

290 papers with code • 73 benchmarks • 7 datasets

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Use these libraries to find Continuous Control models and implementations
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Most implemented papers

Continuous control with deep reinforcement learning

ray-project/ray 9 Sep 2015

We adapt the ideas underlying the success of Deep Q-Learning to the continuous action domain.

Simple random search provides a competitive approach to reinforcement learning

modestyachts/ARS 19 Mar 2018

A common belief in model-free reinforcement learning is that methods based on random search in the parameter space of policies exhibit significantly worse sample complexity than those that explore the space of actions.

High-Dimensional Continuous Control Using Generalized Advantage Estimation

labmlai/annotated_deep_learning_paper_implementations 8 Jun 2015

Policy gradient methods are an appealing approach in reinforcement learning because they directly optimize the cumulative reward and can straightforwardly be used with nonlinear function approximators such as neural networks.

Benchmarking Deep Reinforcement Learning for Continuous Control

rllab/rllab 22 Apr 2016

Recently, researchers have made significant progress combining the advances in deep learning for learning feature representations with reinforcement learning.

Dream to Control: Learning Behaviors by Latent Imagination

danijar/dreamer ICLR 2020

Learned world models summarize an agent's experience to facilitate learning complex behaviors.

Conservative Q-Learning for Offline Reinforcement Learning

aviralkumar2907/CQL NeurIPS 2020

We theoretically show that CQL produces a lower bound on the value of the current policy and that it can be incorporated into a policy learning procedure with theoretical improvement guarantees.

Parameter Space Noise for Exploration

tensorflow/models ICLR 2018

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

Off-Policy Deep Reinforcement Learning without Exploration

sfujim/BCQ 7 Dec 2018

Many practical applications of reinforcement learning constrain agents to learn from a fixed batch of data which has already been gathered, without offering further possibility for data collection.