Papers MuJoCo
“MuJoCo” 태그가 달린 논문 677편 · 필터 해제
Aligning Humans and Robots via Reinforcement Learning from Implicit Human Feedback
Conventional reinforcement learning (RL) ap proaches often struggle to learn effective policies under sparse reward conditions, necessitating the manual design of complex, task-specific reward functions. To address this …
EEGMuJoCoreinforcement-learningReinforcement Learning+1Turning Sand to Gold: Recycling Data to Bridge On-Policy and Off-Policy Learning via Causal Bound
Deep reinforcement learning (DRL) agents excel in solving complex decision-making tasks across various domains. However, they often require a substantial number of training steps and a vast experience replay buffer, lead…
counterfactualDecision MakingDeep Reinforcement LearningMuJoCo+1Deep Reinforcement Learning with Gradient Eligibility Traces
Achieving fast and stable off-policy learning in deep reinforcement learning (RL) is challenging. Most existing methods rely on semi-gradient temporal-difference (TD) methods for their simplicity and efficiency, but are …
Deep Reinforcement LearningMuJoCoreinforcement-learningReinforcement Learning+1Safe Domain Randomization via Uncertainty-Aware Out-of-Distribution Detection and Policy Adaptation
Deploying reinforcement learning (RL) policies in real-world involves significant challenges, including distribution shifts, safety concerns, and the impracticality of direct interactions during policy refinement. Existi…
MuJoCoOut-of-Distribution DetectionOut of Distribution (OOD) DetectionReinforcement Learning (RL)Detecting and Mitigating Reward Hacking in Reinforcement Learning Systems: A Comprehensive Empirical Study
Reward hacking in Reinforcement Learning (RL) systems poses a critical threat to the deployment of autonomous agents, where agents exploit flaws in reward functions to achieve high scores without fulfilling intended obje…
MuJoCoRecommendation SystemsReinforcement Learning (RL)Generalized Adaptive Transfer Network: Enhancing Transfer Learning in Reinforcement Learning Across Domains
Transfer learning in Reinforcement Learning (RL) enables agents to leverage knowledge from source tasks to accelerate learning in target tasks. While prior work, such as the Attend, Adapt, and Transfer (A2T) framework, a…
Atari GamesChatbotComputational EfficiencyDeep Learning+6rQdia: Regularizing Q-Value Distributions With Image Augmentation
rQdia regularizes Q-value distributions with augmented images in pixel-based deep reinforcement learning. With a simple auxiliary loss, that equalizes these distributions via MSE, rQdia boosts DrQ and SAC on 9/12 and 10/…
continuous-controlContinuous ControlDeep Reinforcement LearningImage Augmentation+1Beyond-Expert Performance with Limited Demonstrations: Efficient Imitation Learning with Double Exploration
Imitation learning is a central problem in reinforcement learning where the goal is to learn a policy that mimics the expert's behavior. In practice, it is often challenging to learn the expert policy from a limited numb…
Imitation LearningMuJoCoADDQ: Adaptive Distributional Double Q-Learning
Bias problems in the estimation of $Q$-values are a well-known obstacle that slows down convergence of $Q$-learning and actor-critic methods. One of the reasons of the success of modern RL algorithms is partially a direc…
Distributional Reinforcement LearningMuJoCoQ-LearningLearning Task Belief Similarity with Latent Dynamics for Meta-Reinforcement Learning
Meta-reinforcement learning requires utilizing prior task distribution information obtained during exploration to rapidly adapt to unknown tasks. The efficiency of an agent's exploration hinges on accurately identifying …
Meta Reinforcement LearningMuJoCoHard Contacts with Soft Gradients: Refining Differentiable Simulators for Learning and Control
Contact forces pose a major challenge for gradient-based optimization of robot dynamics as they introduce jumps in the system's velocities. Penalty-based simulators, such as MuJoCo, simplify gradient computation by softe…
MuJoCoThe Courage to Stop: Overcoming Sunk Cost Fallacy in Deep Reinforcement Learning
Off-policy deep reinforcement learning (RL) typically leverages replay buffers for reusing past experiences during learning. This can help improve sample efficiency when the collected data is informative and aligned with…
Deep Reinforcement LearningMuJoCoReinforcement Learning (RL)Wasserstein Barycenter Soft Actor-Critic
Deep off-policy actor-critic algorithms have emerged as the leading framework for reinforcement learning in continuous control domains. However, most of these algorithms suffer from poor sample efficiency, especially in …
continuous-controlContinuous ControlMuJoCoModular Recurrence in Contextual MDPs for Universal Morphology Control
A universal controller for any robot morphology would greatly improve computational and data efficiency. By utilizing contextual information about the properties of individual robots and exploiting their modular structur…
Deep Reinforcement LearningMuJoCoMOBODY: Model Based Off-Dynamics Offline Reinforcement Learning
We study the off-dynamics offline reinforcement learning problem, where the goal is to learn a policy from offline datasets collected from source and target domains with mismatched transition. Existing off-dynamics offli…
Data AugmentationmodelMuJoCoOffline RL+2Accelerating Diffusion Models in Offline RL via Reward-Aware Consistency Trajectory Distillation
Although diffusion models have achieved strong results in decision-making tasks, their slow inference speed remains a key limitation. While the consistency model offers a potential solution, its applications to decision-…
Decision MakingMuJoCoOffline RLLLMs for sensory-motor control: Combining in-context and iterative learning
We propose a method that enables large language models (LLMs) to control embodied agents by directly mapping continuous observation vectors to continuous action vectors. Initially, the LLMs generate a control strategy ba…
MuJoCoEnhanced DACER Algorithm with High Diffusion Efficiency
Due to their expressive capacity, diffusion models have shown great promise in offline RL and imitation learning. Diffusion Actor-Critic with Entropy Regulator (DACER) extended this capability to online RL by using the r…
DenoisingImitation LearningMuJoCoOffline RL+1ADG: Ambient Diffusion-Guided Dataset Recovery for Corruption-Robust Offline Reinforcement Learning
Real-world datasets collected from sensors or human inputs are prone to noise and errors, posing significant challenges for applying offline reinforcement learning (RL). While existing methods have made progress in addre…
DenoisingMuJoCoOffline RLReinforcement Learning (RL)Measure gradients, not activations! Enhancing neuronal activity in deep reinforcement learning
Deep reinforcement learning (RL) agents frequently suffer from neuronal activity loss, which impairs their ability to adapt to new data and learn continually. A common method to quantify and address this issue is the tau…
Deep Reinforcement LearningMuJoCoReinforcement Learning (RL)