Search Results for author:
Found 18 papers, 11 papers with code
The Power of the Senses: Generalizable Manipulation from Vision and Touch through Masked Multimodal Learning
For tasks requiring object manipulation, we seamlessly and effectively exploit the complementarity of our senses of vision and touch.
Language Reward Modulation for Pretraining Reinforcement Learning
Using learned reward functions (LRFs) as a means to solve sparse-reward reinforcement learning (RL) tasks has yielded some steady progress in task-complexity through the years.
Imitating Graph-Based Planning with Goal-Conditioned Policies
Recently, graph-based planning algorithms have gained much attention to solve goal-conditioned reinforcement learning (RL) tasks: they provide a sequence of subgoals to reach the target-goal, and the agents learn to execute subgoal-conditioned policies.
Multi-View Masked World Models for Visual Robotic Manipulation
In this paper, we investigate how to learn good representations with multi-view data and utilize them for visual robotic manipulation.
HARP: Autoregressive Latent Video Prediction with High-Fidelity Image Generator
Video prediction is an important yet challenging problem; burdened with the tasks of generating future frames and learning environment dynamics.
Masked World Models for Visual Control
Yet the current approaches typically train a single model end-to-end for learning both visual representations and dynamics, making it difficult to accurately model the interaction between robots and small objects.
Model-based Reinforcement Learning
Reinforcement Learning (RL)
+1
Reinforcement Learning with Action-Free Pre-Training from Videos
Our framework consists of two phases: we pre-train an action-free latent video prediction model, and then utilize the pre-trained representations for efficiently learning action-conditional world models on unseen environments.
SURF: Semi-supervised Reward Learning with Data Augmentation for Feedback-efficient Preference-based Reinforcement Learning
In order to leverage unlabeled samples for reward learning, we infer pseudo-labels of the unlabeled samples based on the confidence of the preference predictor.
Object-Aware Regularization for Addressing Causal Confusion in Imitation Learning
Behavioral cloning has proven to be effective for learning sequential decision-making policies from expert demonstrations.
Landmark-Guided Subgoal Generation in Hierarchical Reinforcement Learning
In this paper, we present HIerarchical reinforcement learning Guided by Landmarks (HIGL), a novel framework for training a high-level policy with a reduced action space guided by landmarks, i. e., promising states to explore.
Efficient Exploration
Hierarchical Reinforcement Learning
+2
Autoregressive Latent Video Prediction with High-Fidelity Image Generator
Video prediction is an important yet challenging problem; burdened with the tasks of generating future frames and learning environment dynamics.
Offline-to-Online Reinforcement Learning via Balanced Replay and Pessimistic Q-Ensemble
Recent advance in deep offline reinforcement learning (RL) has made it possible to train strong robotic agents from offline datasets.
State Entropy Maximization with Random Encoders for Efficient Exploration
Recent exploration methods have proven to be a recipe for improving sample-efficiency in deep reinforcement learning (RL).
Addressing Distribution Shift in Online Reinforcement Learning with Offline Datasets
As it turns out, fine-tuning offline RL agents is a non-trivial challenge, due to distribution shift – the agent encounters out-of-distribution samples during online interaction, which may cause bootstrapping error in Q-learning and instability during fine-tuning.
Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning
Model-based reinforcement learning (RL) has shown great potential in various control tasks in terms of both sample-efficiency and final performance.
Learning What to Defer for Maximum Independent Sets
Designing efficient algorithms for combinatorial optimization appears ubiquitously in various scientific fields.
Context-aware Dynamics Model for Generalization in Model-Based Reinforcement Learning
Model-based reinforcement learning (RL) enjoys several benefits, such as data-efficiency and planning, by learning a model of the environment's dynamics.
Model-based Reinforcement Learning
reinforcement-learning
+1
Deep Auto-Deferring Policy for Combinatorial Optimization
Designing efficient algorithms for combinatorial optimization appears ubiquitously in various scientific fields.
