Search Results for author:
Found 2 papers, 0 papers with code
Context-Former: Stitching via Latent Conditioned Sequence Modeling
On the other hand, Decision Transformer (DT) abstracts the decision-making as sequence modeling, showcasing competitive performance on offline RL benchmarks, however, recent studies demonstrate that DT lacks of stitching capability, thus exploit stitching capability for DT is vital to further improve its performance.
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.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
