# Continuous Control

290 papers with code • 73 benchmarks • 7 datasets

## Libraries

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

# Continuous control with deep reinforcement learning

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

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

A platform for Applied Reinforcement Learning (Applied RL)

# Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research

The purpose of this technical report is two-fold.

# Simple random search provides a competitive approach to reinforcement learning

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

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

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

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

# Conservative Q-Learning for Offline Reinforcement Learning

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

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

# Off-Policy Deep Reinforcement Learning without Exploration

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.