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Found 8 papers, 2 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.
Addressing Optimism Bias in Sequence Modeling for Reinforcement Learning
Impressive results in natural language processing (NLP) based on the Transformer neural network architecture have inspired researchers to explore viewing offline reinforcement learning (RL) as a generic sequence modeling problem.
BATS: Best Action Trajectory Stitching
Past efforts for developing algorithms in this area have revolved around introducing constraints to online reinforcement learning algorithms to ensure the actions of the learned policy are constrained to the logged data.
Learning to Robustly Negotiate Bi-Directional Lane Usage in High-Conflict Driving Scenarios
We train policies to robustly negotiate with opposing vehicles of an unobservable degree of cooperativeness using multi-agent reinforcement learning (MARL).
Fine-Tuning Offline Reinforcement Learning with Model-Based Policy Optimization
Then, we can optionally enter a second stage where we fine-tune the policy using our novel Model-Based Behavior-Regularized Policy Optimization (MB2PO) algorithm.
Composable Action-Conditioned Predictors: Flexible Off-Policy Learning for Robot Navigation
We show that a simulated robotic car and a real-world RC car can gather data and train fully autonomously without any human-provided labels beyond those needed to train the detectors, and then at test-time be able to accomplish a variety of different tasks.
Self-supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation
To address the need to learn complex policies with few samples, we propose a generalized computation graph that subsumes value-based model-free methods and model-based methods, with specific instantiations interpolating between model-free and model-based.
Uncertainty-Aware Reinforcement Learning for Collision Avoidance
However, practical deployment of reinforcement learning methods must contend with the fact that the training process itself can be unsafe for the robot.
