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04/19/2023
EC^2: Emergent Communication for Embodied Control
Embodied control requires agents to leverage multi-modal pre-training to...
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02/03/2023
AdaptDiffuser: Diffusion Models as Adaptive Self-evolving Planners
Diffusion models have demonstrated their powerful generative capability ...
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11/24/2022
MaskPlace: Fast Chip Placement via Reinforced Visual Representation Learning
Placement is an essential task in modern chip design, aiming at placing ...
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11/23/2022
Prototypical context-aware dynamics generalization for high-dimensional model-based reinforcement learning
The latent world model provides a promising way to learn policies in a c...
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10/09/2022
Decomposed Mutual Information Optimization for Generalized Context in Meta-Reinforcement Learning
Adapting to the changes in transition dynamics is essential in robotic a...
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10/02/2022
EUCLID: Towards Efficient Unsupervised Reinforcement Learning with Multi-choice Dynamics Model
Unsupervised reinforcement learning (URL) poses a promising paradigm to ...
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06/17/2022
CtrlFormer: Learning Transferable State Representation for Visual Control via Transformer
Transformer has achieved great successes in learning vision and language...
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06/08/2022
CO^3: Cooperative Unsupervised 3D Representation Learning for Autonomous Driving
Unsupervised contrastive learning for indoor-scene point clouds has achi...
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05/23/2022
Flow-based Recurrent Belief State Learning for POMDPs
Partially Observable Markov Decision Process (POMDP) provides a principl...
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03/23/2022
Scale-Equivalent Distillation for Semi-Supervised Object Detection
Recent Semi-Supervised Object Detection (SS-OD) methods are mainly based...
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02/21/2022
Don't Touch What Matters: Task-Aware Lipschitz Data Augmentation for Visual Reinforcement Learning
One of the key challenges in visual Reinforcement Learning (RL) is to le...
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08/26/2021
Model-based Chance-Constrained Reinforcement Learning via Separated Proportional-Integral Lagrangian
Safety is essential for reinforcement learning (RL) applied in the real ...
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02/17/2021
Separated Proportional-Integral Lagrangian for Chance Constrained Reinforcement Learning
Safety is essential for reinforcement learning (RL) applied in real-worl...
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02/16/2021
Steadily Learn to Drive with Virtual Memory
Reinforcement learning has shown great potential in developing high-leve...
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12/19/2020
Model-Based Actor-Critic with Chance Constraint for Stochastic System
Safety constraints are essential for reinforcement learning (RL) applied...
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02/28/2020