arXiv research

Safe imitation learning with a safety layer for flexible training.

A new algorithm trains experts to safely guide agents in partially observed environments.

OSIL learns safe policies from unsafe demonstrations.

SafeMIL learns safer policies by avoiding risky behavior from non-preferred trajectories.

New method uses neural nets to control systems safely with disturbances.

We propose a framework for ensuring safe behavior of a reinforcement learning agent when the reward function may be difficult to specify. In order to do this, we rely on the existence of demonstrations from expert policies, and we provide a theoretical framework for the agent to optimize in the space of rewards consist…

Adapts safe policies for exploration in high-risk settings.

QFIL improves offline RL by filtering data to reduce bias and variance.

Bayesian Robust Optimization for Imitation Learning (BROIL) balances risk and reward.

Bayesian REX learns Atari games from demonstrations efficiently.

We present a novel human-aware navigation approach, where the robot learns to mimic humans to navigate safely in crowds. The presented model, referred to as DeepMoTIon, is trained with pedestrian surveillance data to predict human velocity in the environment. The robot processes LiDAR scans via the trained network to n…

H-ReIL learns to drive safely in near-accident scenarios.

The success of automated driving deployment is highly depending on the ability to develop an efficient and safe driving policy. The problem is well formulated under the framework of optimal control as a cost optimization problem. Model based solutions using traditional planning are efficient, but require the knowledge …

We propose the use of Bayesian networks, which provide both a mean value and an uncertainty estimate as output, to enhance the safety of learned control policies under circumstances in which a test-time input differs significantly from the training set. Our algorithm combines reinforcement learning and end-to-end imita…

Paper proposes methods to help autonomous vehicles adapt to unexpected driving scenarios.

B-REX efficiently learns Atari game policies from pixel inputs using Bayesian methods.

New method aligns states for better imitation learning.

End-to-end learnable network for safer self-driving with interpretable intermediate representations.

ADVISOR dynamically balances imitation and reinforcement learning to overcome the imitation gap.

Compared to reinforcement learning, imitation learning (IL) is a powerful paradigm for training agents to learn control policies efficiently from expert demonstrations. However, in most cases, obtaining demonstration data is costly and laborious, which poses a significant challenge in some scenarios. A promising altern…

Reinforcement learning mimics expert behavior.

Guarantees for third-person imitation learning from offline data.

GWIL uses Gromov-Wasserstein distance to align expert and imitation agent states.

New metric solves correspondence problem for robotic arm imitation learning.

Study optimal investment under imitation of decision-changing rates.

Proof shows imitation of expert's reward and solutions in multi-objective optimization.

Researchers improved Minecraft game performance using imitation learning.

Paper tackles robust imitation learning from noisy demonstrations.

We show that a critical vulnerability in adversarial imitation is the tendency of discriminator networks to learn spurious associations between visual features and expert labels. When the discriminator focuses on task-irrelevant features, it does not provide an informative reward signal, leading to poor task performanc…

Paper uses Sinkhorn distances to improve imitation learning effectiveness.

Safe linear stochastic bandits ensure safe exploration with optimal regret.

Imitation from observation is the framework of learning tasks by observing demonstrated state-only trajectories. Recently, adversarial approaches have achieved significant performance improvements over other methods for imitating complex behaviors. However, these adversarial imitation algorithms often require many demo…

New method uses bi-level optimization to learn useful representations for imitation learning.

New self-imitation learning method improves performance in continuous control tasks.

NDI learns from expert demonstrations by estimating occupancy measures.

The paper analyzes the value discrepancies of imitation learning methods.

New method improves imitation learning from expert observations.

New method simplifies and improves imitation learning without adversarial techniques.

We study risk-sensitive imitation learning where the agent's goal is to perform at least as well as the expert in terms of a risk profile. We first formulate our risk-sensitive imitation learning setting. We consider the generative adversarial approach to imitation learning (GAIL) and derive an optimization problem for…

Proposes a method to imitate active learning heuristics for better performance.

Integration of reinforcement learning and imitation learning is an important problem that has been studied for a long time in the field of intelligent robotics. Reinforcement learning optimizes policies to maximize the cumulative reward, whereas imitation learning attempts to extract general knowledge about the traject…

Paper improves AIRL by enhancing policy imitation and addressing reward recovery issues.

Imitation from observation (IfO) is the problem of learning directly from state-only demonstrations without having access to the demonstrator's actions. The lack of action information both distinguishes IfO from most of the literature in imitation learning, and also sets it apart as a method that may enable agents to l…

Relational Mimic improves visual imitation learning from video demonstrations.

Transforms random forests into efficient neural networks using imitation learning.

A new method removes policy optimization in adversarial imitation learning.

The current dominant paradigm for imitation learning relies on strong supervision of expert actions to learn both 'what' and 'how' to imitate. We pursue an alternative paradigm wherein an agent first explores the world without any expert supervision and then distills its experience into a goal-conditioned skill policy …

Recent work has demonstrated that problems-- particularly imitation learning and structured prediction-- where a learner's predictions influence the input-distribution it is tested on can be naturally addressed by an interactive approach and analyzed using no-regret online learning. These approaches to imitation learni…