Reinforcement learning mimics expert behavior.
problem Learning from expert demonstrations in reinforcement learning.
method Reduction to reinforcement learning with a stationary reward.
result Expert reward can be recovered and imitation learning is bounded.
New method minimizes f-divergence for better imitation learning.
problem Learning from multi-modal demonstrations, especially interpolating between modes.
method Minimizing reverse KL divergence or I-projection for any f-divergence.
result Our method reliably imitates multi-modal behaviors better than existing methods.
The paper analyzes the value discrepancies of imitation learning methods.
problem Analyzing the value discrepancies of imitation learning methods.
method Discrepancy propagation analysis for infinite-horizon settings.
result GAIL has less compounding errors than behavioral cloning.
New algorithm reduces sample complexity for imitation learning.
problem High sample complexity limits deployment of adversarial imitation algorithms.
method Trajectory-centric reinforcement learning ideas.
result Improved learning rate and efficiency in imitation tasks.
Compressed imitation learning uses simplicity priors for efficient expert behavior copying.
problem Efficiently learn expert behaviors with minimal data.
method Utilizes policy simplicity as a prior for sample-efficient imitation learning.
result Significantly higher scores achieved with limited expert demonstrations.
Paper proposes Imitative Models combining IL and planning for flexible goal achievement.
problem Difficult to direct IL to arbitrary goals and specify reward functions for goal-directed planning.
method Imitative Models are probabilistic predictive models that plan interpretable expert-like trajectories.
result Imitative Models outperform IL and planning approaches in a dynamic autonomous driving task.
ProMoD models human race drivers with probabilistic movement primitives and neural networks.
problem Challenging task of modeling human driver behavior due to variability and complexity.
method Modular framework with Probabilistic Movement Primitives, clothoids, and neural networks.
result Significant advantages in imitation accuracy and robustness compared to other algorithms.
Extends InfoGAIL to improve imitation learning over extended periods.
problem Stable imitation of extended expert behavior from multi-modal data.
method Incorporates 'burn-in demonstrations' to condition policies at test time.
result Improves mutual information and long-term imitation of expert behavior.
New method uses bi-level optimization to learn useful representations for imitation learning.
problem Learning useful representations for multiple tasks in imitation learning settings.
method Formulates representation learning as a bi-level optimization problem.
result Bi-level optimization framework provides sample complexity benefits for imitation learning.
Game aims to improve social interactions for teenagers with ASD.
problem Improving social interactions for teenagers with ASD.
method Presentation of game structure, skeleton detection, and imitation learning methods.
result Game structure and support tools for skeleton detection and imitation learning presented.
PWIL learns agent behavior from expert using Wasserstein distance.
problem Learning agent behavior from expert demonstrations efficiently.
method Primal Wasserstein Imitation Learning (PWIL) with offline reward function.
result PWIL achieves expert behavior on MuJoCo tasks with minimal interactions.
Combines BC and GAIL for efficient imitation learning.
problem Efficient imitation learning without reward signals.
method Integrates Behavior Cloning and Generative Adversarial Imitation Learning.
result Combination leads to stable and sample-efficient learning.
This paper surveys imitation learning methods and challenges.
problem Difficulty in programming AI systems in complex environments.
method Learning from expert demonstrations to adapt behaviors.
result Overview of recent advances and emerging areas in IL.
Paper tackles covariate shift in offline IL using less proficient behavior data.
problem Mitigating covariate shift in Imitation Learning with limited data coverage.
method Model-based IL from Offline Data (MILO) framework.
result MILO can combat covariate shift even with sub-optimal behavior policy data.
New technique improves imitation learning by preventing local minima and exploring states.
problem Behavioral cloning gets stuck in local minima and lacks effective exploration.
method Two-phase model with sampling mechanisms and self-attention modules.
result Significantly outperforms previous state-of-the-art in various environments.
Aims to learn from multiple unpredictable teachers with minimal interaction.
problem Learning from multiple non-deterministic teachers with low interaction cost.
method Develops a framework and an active learning algorithm to estimate a distribution over policy space.
result Significantly reduces interaction with teachers without compromising performance.
Triple-GAIL learns from multiple sources to improve imitation learning for complex behaviors.
problem Limited scalability of GAIL in real-world scenarios like autonomous vehicles.
method Integrates expert demonstrations and generated experiences with an auxiliary skill selector.
result Triple-GAIL outperforms state-of-the-art methods in learning complex behaviors.
New model learns better policies from expert demonstrations with higher efficiency.
problem Learning accurate policies from expert demonstrations with high efficiency.
method Generative adversarial imitation learning (GAIL) model that learns f-divergence automatically. result Learns better policies with higher data efficiency in physics-based control tasks.
The paper uses a novel framework to learn option prices by imitating principal investor behavior.
problem Challenges in modeling stock price changes and decision making in equity markets.
method Non-deterministic Markov decision process, Bayesian deep neural network, reinforcement learning.
result Optimal option prices learned through imitation of principal investor behavior.
DSAC improves robustness of imitation learning.
problem Learning from few expert data and distribution shift.
method DSAC uses adversarial reward function.
result DSAC performs well on PyBullet environments.
Unified framework for imitation learning via moment matching.
problem Closing the gap between imitation and real-world performance.
method Classifying imitation learning algorithms based on reward or action-value moment matching, considering adversarial divergences.
result Derivation of bounds on policy performance for all algorithms in each class, and introduction of moment recoverability.
New framework for multi-agent imitation learning in complex environments.
problem Multi-agent settings with multiple Nash equilibria and non-stationary environments.
method Generalized inverse reinforcement learning and actor-critic algorithm.
result Practical multi-agent imitation learning with good empirical performance.
Improves imitation learning in RL by learning reward function efficiently.
problem Lack of effective reward function approximation in AIRL for imitation tasks.
method Proposes Off-Policy AIRL that combines adversarial learning with efficient reward function approximation.
result Shows superior imitation performance and efficiency compared to state-of-the-art AIL algorithms.
SQIL uses a simple reward strategy to encourage long-horizon imitation of expert demonstrations.
problem Challenges in imitation learning with high-dimensional, continuous observations and unknown dynamics.
method Imitates expert demonstrations by providing a constant reward of +1 for matching actions in demonstrated states, and 0 for all others.
result Empirically outperforms behavioral cloning and achieves competitive results compared to GAIL.
ICIL learns policies invariant to multiple environments, improving generalization.
problem Learning policies from multiple environments leads to spurious correlations.
method ICIL learns invariant feature representations and a matching imitation policy.
result ICIL policies generalize better to unseen environments.
SafeMIL learns safer policies by avoiding risky behavior from non-preferred trajectories.
problem Learning safe imitation policies from non-preferred trajectories in risky environments.
method SafeMIL uses Multiple Instance Learning to learn a cost function from non-preferred trajectories.
result SafeMIL learns a safer policy that avoids non-preferred behaviors without sacrificing reward performance.
New algorithm robustly learns from corrupted demonstrations, even with constant fraction of noise.
problem Learning from corrupted demonstrations where a fraction of data is noise or outliers.
method Proposes a novel robust algorithm using a Median-of-Means (MOM) objective.
result Guarantees accurate policy estimation even with constant fraction of outliers, similar to classical methods in expert demonstration settings.
New metric solves correspondence problem for robotic arm imitation learning.
problem Establishing corresponding states and actions between different robotic arms.
method Introducing a distance measure between dissimilar robotic arms and using it as a loss function.
result The distance measure effectively learns imitation policies by minimizing distance between robotic arms.
Interactive IL beats BC by state-wise annotation cost.
problem Behavior Cloning struggles with annotation cost in sequential decision making.
method Proved Stagger and Warm Stagger algorithms to outperform BC.
result Interactive and hybrid IL methods outperform BC with state-wise annotation.
Behavioral cloning fails due to ignoring causal structure, leading to worse performance.
problem Behavioral cloning's failure to account for causal structure causes worse performance.
method Investigates and proposes interventions to correct causal misidentification.
result Causal misidentification occurs in various domains and can be mitigated.
The paper introduces new metrics for evaluating generative models of behavior.
problem Lack of quantitative evaluation criteria for unsupervised behavior discovery.
method Proposed and investigated several metrics for generative models of behavior.
result The proposed metrics correspond with biologists' intuitions and allow for model evaluation and bias understanding.
Transforms random forests into efficient neural networks using imitation learning.
problem Inefficient architectures of existing methods for transforming random forests into neural networks.
method Generates training data from a random forest and learns a neural network to imitate its behavior.
result Implicit transformation creates efficient neural networks with better generalization.
New method infers latent policies from observations for imitation learning.
problem Learning from observation without expert actions.
method Characterizes causal effects and predicts likelihood of latent actions; uses action alignment for mapping.
result Corrected labeling of latent actions improves imitation performance.
Paper develops a framework for learning interpretable representations of sequential decision behavior.
problem Obtaining a transparent description of existing behavior.
method Inverse decision modeling framework, formalizing both forward and inverse problems.
result Learning interpretable representations of behavior, including suboptimal actions, biased beliefs, and imperfect knowledge.
Paper proposes a method to learn goal-reaching behaviors from scratch using imitation learning.
problem Current reinforcement learning algorithms are brittle and require expert demonstrations.
method Iterated supervised learning where agents relabel and imitate generated trajectories.
result Improved goal-reaching performance and robustness over current RL algorithms.
Scaling up model and data size improves imitation learning in single-agent games.
problem Limited recovery of expert behavior in single-agent games using imitation learning.
method Investigate the effect of scaling model and data size on imitation learning performance.
result IL loss and mean return scale with compute budget, resulting in power laws.
Behavior cloning can achieve horizon-independent sample complexity in offline imitation learning.
problem Sample complexity in imitation learning increases with problem horizon.
method New analysis of behavior cloning with logarithmic loss.
result Behavior cloning can achieve linear dependence on horizon in offline IL under dense rewards.
The paper provides theoretical guarantees for behavior cloning using generative models.
problem Behavior cloning of complex expert demonstrations using generative models.
method The paper proposes a theoretical framework invoking low-level controllers to stabilize imitation around expert demonstrations. It shows that with suitable low-level stability guarantees and powerful generative models, pure supervised behavior cloning can match expert trajectories.
result The paper proves that with a suitable low-level stability guarantee and a powerful enough generative model, pure supervised behavior cloning can generate trajectories matching the per-time step distribution of essentially arbitrary expert trajectories in an optimal transport cost.
Paper tackles imitation learning with sparse rewards and heterogeneous actions.
problem Challenges of imitation learning with sparse rewards and different actions.
method Proposes a method that balances imitation and reinforcement learning objectives.
result Agent efficiently leverages sparse rewards and learns from different actions.
New method recovers diverse policies from expert data using state-action pair weighting.
problem Recovering diverse policies from expert trajectories.
method Pointwise mutual information weighted behavioral cloning.
result Effective in focusing on state-action pairs most representative of the style.
SAIL improves AIL by weighting adversarial rewards with support estimation.
problem Training instability and reward bias in AIL.
method Support-weighted Adversarial Imitation Learning (SAIL) extends AIL with support estimation to improve reinforcement signals.
result SAIL achieves better performance and stability on benchmark tasks.
CompILE learns reusable segments from demonstrations for hierarchical task execution.
problem Learning reusable, variable-length segments of hierarchical behavior from demonstrations.
method Unsupervised, fully-differentiable sequence segmentation module for latent encoding and re-composition.
result Model generalizes to longer sequences and unseen environments, learns task boundaries and event encodings.
New method simplifies and improves imitation learning without adversarial techniques.
problem Stable optimization and convergence issues in adversarial imitation learning methods.
method Proposes a non-adversarial framework for imitation learning, providing stronger convergence guarantees.
result Shows AIRL as a special case and derives new algorithms for offline imitation learning.
The \$-Game was recently introduced as an extension of the Minority Game. In this paper we compare this model with the well know Minority Game and the Majority Game models. Due to the inter-temporal nature of the market payoff, we introduce a two step transaction with single and mixed group of interacting traders. When…
In this work, we propose a method for learning driver models that account for variables that cannot be observed directly. When trained on a synthetic dataset, our models are able to learn encodings for vehicle trajectories that distinguish between four distinct classes of driver behavior. Such encodings are learned wit…
New framework for learning policies that converge in out-of-sample regions.
problem Reliable out-of-sample recovery in imitation learning.
method Contractive dynamical systems and recurrent equilibrium networks.
result Policy rollouts converge regardless of perturbations, enabling efficient OOS recovery.
Continuous control imitation learning fails if expert actions are smooth.
problem Continuous control imitation learning fails if expert actions are smooth.
method Study of imitation learning in discrete-time, continuous state-and-action control systems.
result Any smooth, deterministic imitator policy suffers exponentially larger error than the expert.
A new approach for learning from expert demonstrations using multiple perspectives.
problem Learning from expert demonstrations with limited information from different perspectives.
method Generative adversarial network-based active learning approach.
result Our approach effectively learns from expert demonstrations and highlights the importance of architectural choices.