arXiv research

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

Paper proposes interpretable RL policies from a mixture of experts.

New model learns better policies from expert demonstrations with higher efficiency.

Paper proposes a method to learn and exceed expert demonstrations in unknown reward environments.

Variational Proximal Policy Optimization improves reinforcement learning from human feedback.

Optimal policies identified for learning systems with a malicious expert.

LEARN-SAM improves RL from sub-optimal demonstrations by localizing expert policies and selectively using demonstrations.

This work improves transferability of rewards inferred from expert demonstrations.

Continuous control imitation learning fails if expert actions are smooth.

We consider the problem of imitation learning from a finite set of expert trajectories, without access to reinforcement signals. The classical approach of extracting the expert's reward function via inverse reinforcement learning, followed by reinforcement learning is indirect and may be computationally expensive. Rece…

New method uses variational inference to handle confounding in imitation learning.

KL-regularized RL from expert demos can lead to slow, unstable learning.

A new method for ILO with transition model disparity using an intermediary policy.

Learning from demonstration (LfD) is the process of building behavioral models of a task from demonstrations provided by an expert. These models can be used e.g. for system control by generalizing the expert demonstrations to previously unencountered situations. Most LfD methods, however, make strong assumptions about …

New findings explain why online methods outperform offline methods in noisy expert feedback settings.

Improves imitation learning in RL by learning reward function efficiently.

Framework learns robust control policies from expert demonstrations.

ICIL learns policies invariant to multiple environments, improving generalization.

Develops statistical framework for resolving reward function ambiguity in inverse reinforcement learning.

Imitation learning is an effective alternative approach to learn a policy when the reward function is sparse. In this paper, we consider a challenging setting where an agent and an expert use different actions from each other. We assume that the agent has access to a sparse reward function and state-only expert observa…

Imitation learning (IL) consists of a set of tools that leverage expert demonstrations to quickly learn policies. However, if the expert is suboptimal, IL can yield policies with inferior performance compared to reinforcement learning (RL). In this paper, we aim to provide an algorithm that combines the best aspects of…

The paper proposes a method to explain expert decisions by modeling preferences with 'what if' outcomes.

Reinforcement learning improves online matching by combining expert policies.

This work explains why online imitation learning improves faster than theory predicts.

New algorithm tackles dynamic query routing to multiple embedding models.

Recent work on imitation learning has generated policies that reproduce expert behavior from multi-modal data. However, past approaches have focused only on recreating a small number of distinct, expert maneuvers, or have relied on supervised learning techniques that produce unstable policies. This work extends InfoGAI…

Pretraining with expert demonstrations have been found useful in speeding up the training process of deep reinforcement learning algorithms since less online simulation data is required. Some people use supervised learning to speed up the process of feature learning, others pretrain the policies by imitating expert dem…

Existing imitation learning approaches often require that the complete demonstration data, including sequences of actions and states, are available. In this paper, we consider a more realistic and difficult scenario where a reinforcement learning agent only has access to the state sequences of an expert, while the expe…

MAPS and MAPS-SE learn from multiple suboptimal experts to improve policies efficiently.

Dual-structured method improves cross-domain imitation learning.

We introduce a novel apprenticeship learning algorithm to learn an expert's underlying reward structure in off-policy model-free \emph{batch} settings. Unlike existing methods that require a dynamics model or additional data acquisition for on-policy evaluation, our algorithm requires only the batch data of observed ex…

In this paper we propose a novel gradient algorithm to learn a policy from an expert's observed behavior assuming that the expert behaves optimally with respect to some unknown reward function of a Markovian Decision Problem. The algorithm's aim is to find a reward function such that the resulting optimal policy matche…

We consider a problem of learning the reward and policy from expert examples under unknown dynamics. Our proposed method builds on the framework of generative adversarial networks and introduces the empowerment-regularized maximum-entropy inverse reinforcement learning to learn near-optimal rewards and policies. Empowe…

AceIRL learns expert reward from active exploration.

We generalise the problem of inverse reinforcement learning to multiple tasks, from multiple demonstrations. Each one may represent one expert trying to solve a different task, or as different experts trying to solve the same task. Our main contribution is to formalise the problem as statistical preference elicitation,…

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 …

Novel framework learns policies from noisy expert demonstrations.

Reinforcement learning mimics expert behavior.

Paper tackles covariate shift in offline IL using less proficient behavior data.

A new framework uses deep RL to aggregate expert advice for better portfolio management.

New method recovers diverse policies from expert data using state-action pair weighting.

Paper explores limits of imitation learning in MDPs, setting new suboptimality bounds.

Monte Carlo Tree Search (MCTS) algorithms perform simulation-based search to improve policies online. During search, the simulation policy is adapted to explore the most promising lines of play. MCTS has been used by state-of-the-art programs for many problems, however a disadvantage to MCTS is that it estimates the va…

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…

Imitation learning seeks to learn an expert policy from sampled demonstrations. However, in the real world, it is often difficult to find a perfect expert and avoiding dangerous behaviors becomes relevant for safety reasons. We present the idea of \textit{learning to avoid}, an objective opposite to imitation learning …

Learning near-optimal behaviour from an expert's demonstrations typically relies on the assumption that the learner knows the features that the true reward function depends on. In this paper, we study the problem of learning from demonstrations in the setting where this is not the case, i.e., where there is a mismatch …

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…

A new method learns policies from sub-optimal trajectories.