Continuous Control
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Benchmarks
PyBullet Ant
PyBullet HalfCheetah
PyBullet Hopper
PyBullet Walker2D
Lunar Lander (OpenAI Gym)
cartpole.balance_sparse
cartpole.swingup
cheetah.run
finger.turn_hard
walker.stand
walker.walk
2D Walker
Acrobot
Ant
Ant + Gathering
Ant + Maze
Cart Pole (OpenAI Gym)
Cart-Pole Balancing
Double Inverted Pendulum
Full Humanoid
Half-Cheetah
Hopper
Inverted Pendulum
Mountain Car
Simple Humanoid
Swimmer
Swimmer + Gathering
Swimmer + Maze
acrobot.swingup
ball_in_cup.catch
cartpole.balance
cartpole.swingup_sparse
finger.spin
finger.turn_easy
fish.swim
hopper.hop
hopper.stand
humanoid.run
manipulator.insert_ball
manipulator.insert_peg
pendulum.swingup
quadruped.run
quadruped.walk
reacher.easy
reacher.hard
walker.run
Most implemented
Proximal Policy Optimization Algorithms
Continuous control with deep reinforcement learning
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
Addressing Function Approximation Error in Actor-Critic Methods
Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research
Simple random search provides a competitive approach to reinforcement learning
Papers
FERPO: Forward Entropy-Regularized Policy Optimization
Several state-of-the-art methods for online reinforcement learning in continuous control improve policies using action gradients of a learned critic. However, critics are typically trained to predict returns, and accurat…
Reinforcement LearningContinuous ControlControllable Affective Generation via Latent Vector Steering
Large Language Models (LLMs) often produce emotionally flattened responses after alignment, limiting their effectiveness in affect-sensitive applications. In this paper, we propose EmoVec, a lightweight framework for con…
Continuous ControlGraph-Operator World Models for Morphology-Parameter Generalization in Continuous Control
World models for continuous control are commonly trained for a fixed physical system and can degrade when known morphology parameters such as link lengths, masses, damping, and actuation change. Existing approaches often…
Continuous ControlOrthogonal JEPA: Factorized Predictive States for Latent World Models
World models construct latent states that support prediction, planning, and reasoning about an underlying system. Joint-embedding predictive architectures (JEPAs) offer a direct way to learn such states by predicting tar…
Continuous ControlTo Go Far, Go Together: Diverse Preferences Induce a Curriculum for Reward Optimization
Learning a reward model from human feedback and optimizing a policy against it is one approach to aligning AI systems with individual users. From a fairness perspective, existing work improves such alignment by developin…
Continuous ControlAn Omitted Mode Is a Rare Rule: The Sampling-Verification Danger Law in Continuous Code World Models
In the Code World Model paradigm an LLM synthesizes an executable world model that a classical planner searches, and the model is accepted when it reproduces sampled transitions. We ask what that acceptance certifies in …
Continuous Control