Search Results for author:
Found 11 papers, 4 papers with code
DIDI: Diffusion-Guided Diversity for Offline Behavioral Generation
The goal of DIDI is to learn a diverse set of skills from a mixture of label-free offline data.
Reinformer: Max-Return Sequence Modeling for Offline RL
In this work, we introduce the concept of max-return sequence modeling which integrates the goal of maximizing returns into existing sequence models.
Context-Former: Stitching via Latent Conditioned Sequence Modeling
Offline reinforcement learning (RL) algorithms can learn better decision-making compared to behavior policies by stitching the suboptimal trajectories to derive more optimal ones.
A dynamical clipping approach with task feedback for Proximal Policy Optimization
Different from previous clipping approaches, we consider increasing the maximum cumulative Return in reinforcement learning (RL) tasks as the preference of the RL task, and propose a bi-level proximal policy optimization paradigm, which involves not only optimizing the policy but also dynamically adjusting the clipping bound to reflect the preference of the RL tasks to further elevate the training outcomes and stability of PPO.
RSG: Fast Learning Adaptive Skills for Quadruped Robots by Skill Graph
Developing robotic intelligent systems that can adapt quickly to unseen wild situations is one of the critical challenges in pursuing autonomous robotics.
Improving Offline-to-Online Reinforcement Learning with Q Conditioned State Entropy Exploration
Studying how to fine-tune offline reinforcement learning (RL) pre-trained policy is profoundly significant for enhancing the sample efficiency of RL algorithms.
STRAPPER: Preference-based Reinforcement Learning via Self-training Augmentation and Peer Regularization
Due to the existence of similarity trap, such consistency regularization improperly enhances the consistency possiblity of the model's predictions between segment pairs, and thus reduces the confidence in reward learning, since the augmented distribution does not match with the original one in PbRL.
Design from Policies: Conservative Test-Time Adaptation for Offline Policy Optimization
Naturally, such a paradigm raises three core questions that are not fully answered by prior non-iterative offline RL counterparts like reward-conditioned policy: (q1) What information should we transfer from the inner-level to the outer-level?
Beyond OOD State Actions: Supported Cross-Domain Offline Reinforcement Learning
Offline reinforcement learning (RL) aims to learn a policy using only pre-collected and fixed data.
RotoGBML: Towards Out-of-Distribution Generalization for Gradient-Based Meta-Learning
OOD exacerbates inconsistencies in magnitudes and directions of task gradients, which brings challenges for GBML to optimize the meta-knowledge by minimizing the sum of task gradients in each minibatch.
Behavior Proximal Policy Optimization
Offline reinforcement learning (RL) is a challenging setting where existing off-policy actor-critic methods perform poorly due to the overestimation of out-of-distribution state-action pairs.
