Search Results for author:
Found 4 papers, 0 papers with code
Tractable Joint Prediction and Planning over Discrete Behavior Modes for Urban Driving
Although these models have conventionally been evaluated for open-loop prediction, we show that they can be used to parameterize autoregressive closed-loop models without retraining.
Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous Driving and Zero-Shot Instruction Following
Diffusion-ES samples trajectories during evolutionary search from a diffusion model and scores them using a black-box reward function.
Frustratingly Easy Regularization on Representation Can Boost Deep Reinforcement Learning
In this work, we demonstrate that the learned representation of the $Q$-network and its target $Q$-network should, in theory, satisfy a favorable distinguishable representation property.
MEPG: A Minimalist Ensemble Policy Gradient Framework for Deep Reinforcement Learning
During the training of a reinforcement learning (RL) agent, the distribution of training data is non-stationary as the agent's behavior changes over time.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
