New method aligns states for better imitation learning.
problem Different dynamics models between imitator and expert.
method State alignment-based reinforcement learning.
result Superior performance on various imitation learning settings.
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48 results for “Imitative Models”
New framework integrates imitation and reinforcement learning for better robot performance.
problem Combining reinforcement and imitation learning for intelligent robotics.
method Extends probabilistic generative model framework for reinforcement learning and develops pMDP-MO for Markov decision processes.
result Significantly better performance than reinforcement or imitation learning alone.
NDI learns from expert demonstrations by estimating occupancy measures.
problem Imitation Learning (IL) for complex systems.
method Density estimation of expert's occupancy measure followed by RL.
result NDI achieves state-of-the-art performance on control tasks.
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.
Study optimal investment under imitation of decision-changing rates.
problem Optimal investment under imitation of decision-changing rates.
method Proposed integral disparity to quantify imitation, derived general solution using variational method, analyzed asymptotic properties, validated with real data.
result Investor's optimal decisions under imitation of decision-changing rates.
GPRIL learns efficient policies from few expert demonstrations.
problem Sample-efficient imitation learning for robots.
method Generative predecessor models to match expert demonstrations.
result GPRIL achieves higher sample efficiency on real robots.
ADVISOR dynamically balances imitation and reinforcement learning to overcome the imitation gap.
problem The gap between imitation learning and reinforcement learning when teaching agents have privileged information.
method Adaptive Insubordination (ADVISOR) dynamically weights imitation and reward-based reinforcement learning losses.
result On-the-fly switching with ADVISOR outperforms pure imitation, pure reinforcement learning, and their combinations.
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.
HGAIL learns policies without expert demonstrations.
problem Lack of expert demonstrations in imitation learning.
method Combines hindsight and GAIL to learn policies.
result Comparable performance to current methods, with curriculum learning.
Proposes a method to imitate active learning heuristics for better performance.
problem The performance of active learning heuristics depends on the classifier model and data structure.
method Imitates the selection of the best active learning heuristic using DAGGER.
result Outperforms state-of-the-art imitation learners and heuristics on well-known datasets.
New method uses variational inference to handle confounding in imitation learning.
problem Confounding due to different sensory inputs between expert and imitating agent.
method Train variational inference model to infer expert's latent information and use for latent-conditional policy training.
result Algorithm converges to correct interventional policy and achieves asymptotically optimal performance.
DAC-SSM learns domain-agnostic states for better imitation learning.
problem Domain shifts hinder imitation learning in partially observable tasks.
method DAC-SSM uses adversarial training to remove domain-dependent information from states.
result DAC-SSM achieves comparable performance to experts in sparse reward tasks.
CFIL uses coupled flows to model state distributions for imitation learning.
problem Lack of explicit modeling of state distributions in reinforcement and imitation learning.
method Coupled normalizing flows for state and state-action distributions.
result CFIL achieves state-of-the-art performance on benchmark tasks.
A new imitation learning method uses random search for simple policies, outperforming complex models.
problem Complexity and reward dependency issues in imitation learning.
method Derivative-free optimization with simple linear policies and random search.
result The proposed method achieves competitive performance on MuJoCo locomotion tasks without a direct reward signal.
Simitate is a benchmark for evaluating imitation learning approaches.
problem Evaluating the performance of imitation learning algorithms.
method Hybrid dataset with realistic RGB and depth streams, ground truth poses, and pre-trained models.
result Supports the development of imitation learning approaches through reproducible evaluation metrics.
End-to-end Dyna-AIL combines planning and learning for faster convergence.
problem Efficient imitation learning with minimal interactions.
method Dyna-like framework integrating model-based planning and model-free learning.
result Our approach converges to optimal policies with fewer interactions.
Curriculum learning and imitation learning improve control over financial time-series data.
problem Improving control performance over complex financial time-series data.
method Data augmentation for curriculum learning and policy distillation for imitation learning.
result Curriculum learning shows significant improvement over time-series control tasks.
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.
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.
Guarantees for third-person imitation learning from offline data.
problem Improving generalizability in imitation learning.
method Problem-dependent statistical learning guarantees for third-person imitation from offline observation.
result Strong performance guarantees for transferred policies in the offline setting.
A new reward learning module improves imitation learning in high-dimensional environments.
problem Challenges in high-dimensional environments for imitation learning.
method Generative model to generate intrinsic reward signals.
result Our method outperforms state-of-the-art IRL methods on Atari games.
TRAIL improves robot imitation learning by focusing on task-relevant features.
problem Discriminator networks learn spurious associations, providing poor reward signals.
method Constrained discriminator optimization to learn task-relevant rewards.
result TRAIL outperforms GAIL and behaviour cloning in robotic manipulation tasks.
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.
Beam search policies learned via imitation learning.
problem Beam search policies are not explicitly learned by models during training.
method Developed a meta-algorithm for learning beam search policies using imitation learning.
result Showed no-regret guarantees for learning beam search policies.
GWIL uses Gromov-Wasserstein distance to align expert and imitation agent states.
problem Cross-domain imitation learning challenges due to different system dimensions and stationary distributions.
method Gromov-Wasserstein Imitation Learning (GWIL) using Gromov-Wasserstein distance.
result GWIL effectively aligns expert and imitation agent states in various continuous control domains.
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 …
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 -divergence automatically.
result Learns better policies with higher data efficiency in physics-based control tasks.
Paper introduces a novel reward function for noisy financial markets using imitation learning.
problem Noisy reward function in financial markets hinders RL agent performance.
method Integrates imitation learning feedback with reinforcement learning to improve reward function design.
result Improves financial performance metrics compared to traditional benchmarks and RL agents.
A proof of imitation of Wasserstein inverse reinforcement learning for multi-objective optimizationcs.LG
Proof shows imitation of expert's reward and solutions in multi-objective optimization.
problem Multi-objective optimization with reward and solution imitation.
method Wasserstein inverse reinforcement learning.
result Wasserstein inverse reinforcement learning enables imitation of expert's reward and solutions in multi-objective optimization.
Researchers improved Minecraft game performance using imitation learning.
problem Achieving state-of-the-art performance in immersive environments like Minecraft.
method Applied imitation learning to Minecraft, optimizing network architecture, loss function, and data augmentation.
result Reported stronger results than previous experiments, reaching second place in a competition.
New method learns belief representations for GAIL in POMDPs.
problem Imitation learning in partially observable Markov decision processes (POMDPs).
method Joint learning of belief module and policy with task-aware imitation loss and belief regularization.
result Our BMIL approach outperforms GAIL and task-agnostic belief learning.
New approach uses inverse reinforcement learning to improve language model training.
problem Training large language models using imitation learning methods.
method Developed a new method of inverse reinforcement learning to optimize sequences directly.
result IRL-based fine-tuning leads to better performance and diversity in language generation.
Algorithm improves imitation learning from visual data in partially observable environments.
problem Imitation learning from visual observations with missing expert actions and partial observability.
method Theoretical analysis and Latent Adversarial Imitation from Observations algorithm combining adversarial and latent representations.
result Latent Adversarial Imitation from Observations achieves state-of-the-art performance in high-dimensional robotic tasks.
Paper tackles robust imitation learning from noisy demonstrations.
problem Learning from noisy demonstrations is challenging.
method Optimizes a classification risk with a symmetric loss, combining pseudo-labeling and co-training.
result Our method is more robust than state-of-the-art methods.
Safe imitation learning with a safety layer for flexible training.
problem Flexible yet safe imitation learning for complex tasks.
method Theory and modular method with a safety layer for continuous policy, adversarial training, and worst-case safety guarantees.
result Robustness advantage of safety layer during training compared to test time.
Proves EM algorithm guarantees for hierarchical imitation learning.
problem Learning hierarchical policies from expert demonstrations.
method Converts hierarchical imitation learning to latent variable model inference, analyzes EM algorithm.
result Proves EM algorithm converges to true parameter under regular conditions.
Active-GRPO improves molecular optimization by actively deciding when to imitate or self-improve.
problem Training robust and efficient molecular optimization models with large language models.
method Active-GRPO combines imitation and reinforcement learning, upgrading references and policies dynamically.
result Improves molecular optimization performance, achieving statistically significant gains.
Paper improves imitation learning from observations by minimizing inverse dynamics disagreement.
problem Challenges in Learning from Observations (LfO) due to incomplete expert guidance.
method IDDM method that minimizes the disagreement between inverse dynamics models of the imitator and expert.
result Empirical results show consistent improvements over other LfO methods.
Paper uses Sinkhorn distances to improve imitation learning effectiveness.
problem Improving imitation learning algorithms by comparing occupancy measures.
method Formulates imitation learning as Sinkhorn distance minimization, combining optimal transport and cosine distances.
result Proposes a new critic network and transport plan that guide imitation learning.
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.
The paper shows how imitation learning can be exploited to extract DRL policies.
problem The confidentiality and integrity of DRL policies are vulnerable to model extraction attacks.
method Developed proof-of-concept attacks using imitation learning techniques.
result Demonstrated the feasibility of launching black-box attacks against DRL policies.
New self-imitation learning method improves performance in continuous control tasks.
problem Improving off-policy learning in continuous control tasks.
method Proposes a n-step lower bound to generalize lower-bound Q-learning and introduces a new family of self-imitation learning algorithms.
result n-step lower bound Q-learning achieves a better trade-off between bias and contraction rate, leading to improved performance.
GASIL encourages agents to imitate past good trajectories in reinforcement learning.
problem Long-term credit assignment in sparse and delayed reward environments.
method Generative Adversarial Imitation Learning (GASIL) framework.
result GASIL improves performance in reinforcement learning tasks with delayed rewards.
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.
CLIC learns to control objects in non-rewarding environments by imitating a mentor.
problem Learning in non-rewarding, multi-object environments with non-observable intentions.
method Curriculum Learning and Imitation (CLIC) for Control.
result CLIC effectively learns and imitates a mentor's interactions to control objects.
New method improves imitation learning from expert observations.
problem Challenges in imitation learning from observation setting.
method Reparameterized Variational Divergence Minimization.
result Our method outperforms baseline approaches in low-dimensional tasks.
MobILE learns from expert demonstrations without access to actions, achieving strong performance guarantees.
problem Imitation Learning from Observations alone (ILFO) where actions are not observed.
method MobILE integrates optimism in the face of uncertainty into distribution matching IL framework, trading off exploration and imitation.
result MobILE provides strong performance guarantees for certain MDP dynamics and demonstrates efficacy on OpenAI Gym tasks.
Survey of methods for learning from observation without requiring expert actions.
problem Lack of access to expert actions in imitation learning.
method Survey and classification of state-only imitation learning (SOIL) methods.
result Identification of open problems and future research directions.