Search Results for author:
Found 4 papers, 1 papers with code
MDDL: A Framework for Reinforcement Learning-based Position Allocation in Multi-Channel Feed
There are two types of data employed to train reinforcement learning (RL) model for position allocation, named strategy data and random data.
RePreM: Representation Pre-training with Masked Model for Reinforcement Learning
Inspired by the recent success of sequence modeling in RL and the use of masked language model for pre-training, we propose a masked model for pre-training in RL, RePreM (Representation Pre-training with Masked Model), which trains the encoder combined with transformer blocks to predict the masked states or actions in a trajectory.
TD3 with Reverse KL Regularizer for Offline Reinforcement Learning from Mixed Datasets
There are two challenges for this setting: 1) The optimal trade-off between optimizing the RL signal and the behavior cloning (BC) signal changes on different states due to the variation of the action coverage induced by different behavior policies.
Exploration by Maximizing Rényi Entropy for Reward-Free RL Framework
In the planning phase, the agent computes a good policy for any reward function based on the dataset without further interacting with the environment.
