Continuous Control
422 papers with code • 73 benchmarks • 10 datasets
Continuous control in the context of playing games, especially within artificial intelligence (AI) and machine learning (ML), refers to the ability to make a series of smooth, ongoing adjustments or actions to control a game or a simulation. This is in contrast to discrete control, where the actions are limited to a set of specific, distinct choices. Continuous control is crucial in environments where precision, timing, and the magnitude of actions matter, such as driving a car in a racing game, controlling a character in a simulation, or managing the flight of an aircraft in a flight simulator.
Benchmarks
These leaderboards are used to track progress in Continuous Control
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Libraries
Use these libraries to find Continuous Control models and implementationsDatasets
OpenAI Gym
D4RL
DeepMind Control Suite
Omniverse Isaac Gym
Lani
MO-Gymnasium
PyBullet
MoCapAct
Latest papers with no code
Probabilistic Actor-Critic: Learning to Explore with PAC-Bayes Uncertainty
We introduce Probabilistic Actor-Critic (PAC), a novel reinforcement learning algorithm with improved continuous control performance thanks to its ability to mitigate the exploration-exploitation trade-off.
Frugal Actor-Critic: Sample Efficient Off-Policy Deep Reinforcement Learning Using Unique Experiences
Efficient utilization of the replay buffer plays a significant role in the off-policy actor-critic reinforcement learning (RL) algorithms used for model-free control policy synthesis for complex dynamical systems.
A Strategy for Preparing Quantum Squeezed States Using Reinforcement Learning
It is exemplified by the application to prepare spin-squeezed states for an open collective spin model where a linear control field is designed to govern the dynamics.
Pulse Width Modulation Method Applied to Nonlinear Model Predictive Control on an Under-actuated Small Satellite
Among various satellite actuators, magnetic torquers have been widely equipped for stabilization and attitude control of small satellites.
Identifying Policy Gradient Subspaces
Policy gradient methods hold great potential for solving complex continuous control tasks.
The Distributional Reward Critic Architecture for Perturbed-Reward Reinforcement Learning
We study reinforcement learning in the presence of an unknown reward perturbation.
A Minimaximalist Approach to Reinforcement Learning from Human Feedback
Our approach is maximalist in that it provably handles non-Markovian, intransitive, and stochastic preferences while being robust to the compounding errors that plague offline approaches to sequential prediction.
Trajectory-Oriented Policy Optimization with Sparse Rewards
The proposed algorithm undergoes evaluation across extensive discrete and continuous control tasks with sparse and misleading rewards.
Adversarially Trained Actor Critic for offline CMDPs
Theoretically, we demonstrate that when the actor employs a no-regret optimization oracle, SATAC achieves two guarantees: (i) For the first time in the offline RL setting, we establish that SATAC can produce a policy that outperforms the behavior policy while maintaining the same level of safety, which is critical to designing an algorithm for offline RL.
Ensemble-based Interactive Imitation Learning
We study interactive imitation learning, where a learner interactively queries a demonstrating expert for action annotations, aiming to learn a policy that has performance competitive with the expert, using as few annotations as possible.
