Survey on stated skein algebras and their representations.
problem None explicitly stated in the abstract.
method None explicitly stated in the abstract.
result None explicitly stated in the abstract.
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48 results for “state representations”
Robots learn state representation from demonstrations.
problem Robots need a compact state representation for efficient interaction.
method Imitation learning using a multi-head neural network.
result Trained representation improves performance and efficiency in reinforcement learning.
Method learns state representations without supervision for Atari games.
problem Learning state representations without supervision.
method Maximizes mutual information across features of neural encoder.
result New benchmark for evaluating representation learning models.
This work improves understanding of reinforcement learning state representations.
problem Lack of precise characterization of how and when state representations generalize.
method Developed a bound on the generalization error based on effective dimension.
result Bound quantifies the tension between generalization and approximation.
Toolbox provides datasets and metrics for state representation learning.
problem Lack of standard evaluation datasets, metrics and tasks for state representation learning.
method Provides a set of environments, data generators, robotic control tasks, metrics and tools.
result Facilitates iterative state representation learning and evaluation in reinforcement learning settings.
The paper proposes an algorithm to learn causal state representations for partially observable environments.
problem Learning task-agnostic state abstractions in partially observable environments.
method The approach involves learning approximate causal state representations from RNNs trained to predict observations given the history.
result The learned state representations are useful for efficient policy learning in reinforcement learning problems with rich observation spaces.
SRL learns low-dimensional state features for better control.
problem Learning abstract features for better control in robotics.
method Various SRL methods involving interaction with the environment.
result SRL helps overcome the curse of dimensionality and improves performance.
Survey categorizes methods for learning state representations in reinforcement learning.
problem Addressing challenges in complex observation spaces for sequential decision making.
method Categorizes six main classes of methods for learning state representations.
result Enhances understanding of state representation learning in reinforcement learning.
State sums for quantum link invariants from a specific representation.
problem Calculating quantum link invariants from a specific representation of U_q(gl_{N|M}).
method Using state sums and representation theory of U_q(gl_{N|M}).
result Explicit relation with Kashaev invariants for the N-th exterior power of the standard representation.
Towards Better Laplacian Representation in Reinforcement Learning with Generalized Graph Drawingcs.LG
New method learns high-quality Laplacian representations for reinforcement learning.
problem Lack of accurate Laplacian representations in large or continuous state spaces.
method Reformulated spectral graph drawing objective to have eigenvectors as unique global minimizer.
result Learned Laplacian representations more faithfully approximate the ground truth.
Continual state learning model using generative replay for RL.
problem Efficiently learn and adapt state representations as the environment changes.
method Variational Auto-Encoders for state representation and Generative Replay for past knowledge.
result Automatic environment change detection and efficient state representation.
Paper learns meaningful state and action representations from MDP trajectories.
problem Learning good state and action representations from MDP trajectories.
method Tensor decomposition, kernelization, importance sampling, low-Tucker-rank approximation.
result The learned state/action abstractions provide accurate approximations to latent block structures.
This work learns latent representations to speed up exploration in complex environments.
problem Challenging exploration in high-dimensional state and action spaces with sparse rewards.
method Representation learning using prior experience to learn effective latent representations.
result Learned latent representations reduce the dimensionality of the search space for effective exploration.
New method improves robot learning from vision with better sample efficiency.
problem Scaling reinforcement learning to real robots from vision.
method State representation learning to extract relevant features.
result Improved sample efficiency and robustness to hyper-parameters.
S-TRIGGER learns state representations for continual learning.
problem Efficiently compress and maintain past knowledge in changing environments.
method Generative Replay with self-triggered environment change detection.
result S-TRIGGER enables fast and high-performing Reinforcement Learning without catastrophic forgetting.
New insights into state representations in RL help design better learning rules.
problem Lack of automatic feature learning in RL for large or continuous state spaces.
method Bootstrapping methods and theoretical analysis of temporal difference learning.
result State representations differ from other auxiliary-task-based approaches.
Efficiently learns sparse low-dimensional Markov chain representations.
problem Learning low-dimensional representations for large-scale Markov chains with sparse structures.
method Formulates as constrained nonnegative matrix factorization and uses gradient descent.
result Proves the effectiveness of the proposed method through convergence analysis.
Graph-based state representation improves deep RL performance.
problem High sample-complexity and starting with a good input representation in deep RL.
method Exploiting the graph structure of MDPs for effective state representation learning.
result Graph-based node representation methods outperform matrix-based methods in grid-world navigation tasks.
Neural architectures learn belief representations for partially observable environments.
problem Learning belief states in partially observable domains.
method One-step frame prediction and contrastive predictive coding (CPC) as objective functions.
result Neural architectures can learn belief representations, encoding both state information and uncertainty.
New LSTM structure improves text representation efficiency.
problem Sequential limitations of Bi-directional LSTMs.
method Sentence-State LSTM with parallel word states and simultaneous information exchange.
result Sentence-State LSTM achieves competitive performance on text benchmarks.
Proves error bounds for state representation in RL using graph spectral features.
problem Addressing the curse of dimensionality in RL with unknown transition graphs.
method Proves upper bounds on approximation error of linear value function approximation using learned spectral features of the state-graph.
result Error bounds scale with algebraic connectivity and eigenvector estimation error.
Study cost-driven state representation learning for control from partial observations.
problem Learning state representation for control from partial and high-dimensional observations.
method Cost-driven state representation learning via predicting cumulative costs.
result Established finite-sample guarantees for near-optimal representation and controller.
Sparse representations improve reinforcement learning performance.
problem TD Learning struggles with large state spaces and simple control tasks.
method Learned sparse representations to reduce state space and support generalization.
result Sparse representations enhance reinforcement learning performance on challenging tasks.
A new approach for exploration in RL using the successor representation.
problem Developing theoretically justified algorithms for exploration in RL.
method The successor representation (SR) and substochastic successor representation (SSR) to incentivize exploration and count observations.
result An algorithm that performs as well as sample-efficient approaches and achieves state-of-the-art performance in Atari games.
PSDs improve RNN performance by predicting future observations.
problem Modeling dynamic processes with unknown latent states.
method Augmenting RNNs with Predictive-State Decoders (PSDs) that target predicting future observations.
result PSDs improve statistical performance of state-of-the-art RNNs with fewer iterations and less data.
Paper introduces SALE for better state-action learning in RL.
problem Challenges in representation learning for low-level states in RL.
method Introduces SALE, a novel approach for learning embeddings of state-action interactions.
result TD7 algorithm significantly outperforms existing continuous control algorithms.
Improved RL for grasping in cluttered scenes using state representation learning.
problem Poor performance of RL methods in grasping diverse objects from raw images.
method Employed state representation learning (SRL) with disentanglement of raw input images.
result Deep RL can learn grasping skills from varied visual inputs.
Study learns state representations from observations for control, proving guarantees.
problem Learning state representations from high-dimensional observations for control.
method Cost-driven approach, learning latent state model to predict costs.
result Proves finite-sample guarantees for near-optimal state representation and controller.
Variational autoencoders improve state representation for hard quantum systems.
problem Simulating and storing quantum states is computationally infeasible.
method Introduced variational autoencoders for quantum state representation.
result Deep networks better represent hard quantum states, suggesting compositional structure.
ReLEX algorithm improves RL efficiency by selecting optimal representations.
problem Improving reinforcement learning efficiency through better representation selection.
method Proposes ReLEX algorithm for both online and offline RL, focusing on bilinear transition kernels.
result ReLEX algorithms achieve optimal or near-optimal performance in both online and offline RL settings.
Extended quantum state result for gl_n weight systems.
problem Quantum states associated with gl_n weight systems.
method Extended Corfield et al. result to all gl_n weight systems.
result All gl_n weight systems are quantum states.
New invariant derived from skein algebra representations at roots of unity.
problem Understanding skein algebras of open surfaces and their invariants.
method Extended skein algebras, constructed isomorphisms, embedding and isomorphism constructions.
result Invariant associated to each skein algebra representation class.
Simple object representations improve model-free RL performance.
problem Current reinforcement learning agents lack object recognition.
method Used simple, feature-engineered object representations with the Rainbow model.
result Object representations significantly boost performance on Atari games.
RPSP networks combine PSRs and RNNs for reinforcement learning in POE.
problem Learning in partially observable environments.
method Recurrent filter with PSR, reactive policy, gradient descent.
result RPSP networks outperform memory-preserving models.
A new method approximates Laplacian eigenvectors for RL efficiently.
problem Efficiently learning state representations in RL.
method General and scalable approach to approximating Laplacian eigenvectors.
result Empirically shows improved performance in RL tasks.
Empirical mode modeling improves state-space analysis of noisy data.
problem Analyzing nonlinear systems with noisy data.
method Combining empirical mode decomposition with empirical dynamic modeling.
result Empirical mode modeling enhances state-space representations in noisy data.
Tabular Q-Learning with learned state abstractions solves continuous control tasks.
problem Challenging reinforcement learning problems in continuous control.
method Learned state abstraction to transform continuous state-space into discrete.
result Tabular Q-Learning with learned abstractions achieves efficient learning in unseen tasks.
SPEDER extracts state-action abstraction from dynamics for reinforcement learning.
problem Curse of dimensionality and limited applicability of spectral methods.
method Spectral Decomposition Representation (SPEDER) that extracts state-action abstraction from dynamics without policy dependence.
result Theoretical analysis establishes sample efficiency in online and offline settings.
Method learns behavioral states from wearable sensor data.
problem Understanding behavioral patterns from sensor data.
method Non-parametric Bayesian approach to model sensor data.
result Learned behavioral states cluster participants into meaningful groups and predict psychological states.
A method to train multi-agent reinforcement learning models without intrinsic rewards.
problem Training multi-agent reinforcement learning models with sparse rewards is challenging.
method A learning-based exploration strategy using variational graph autoencoder to generate initial states.
result The method improves the training and performance of multi-agent reinforcement learning models.
Successor Options discovers reusable skills using landmark states.
problem Discovering reusable skills in reinforcement learning.
method Leverages Successor Representations to build a state space model and learns intra-option policies using a novel pseudo-reward.
result Demonstrates the approach's efficacy on grid-worlds and high-dimensional robotic control environments.
We emphasize some properties of coherent state groups, i.e. groups whose quotient with the stationary groups, are manifolds which admit a holomorphic embedding in a projective Hilbert space. We determine the differential action of the generators of the representation of coherent state groups on the symmetric Fock space…
Improves NILM with multi-label SRC, outperforming state-of-the-art.
problem Non-intrusive load monitoring (NILM) for energy disaggregation.
method Modified multi-label sparse representation based classification (SRC).
result Significant improvement over state-of-the-art techniques with minimal training data.
The paper introduces a method to learn Markov state abstractions for reinforcement learning.
problem Learning Markov state representations in complex environments.
method The paper introduces a novel set of conditions and a training procedure combining inverse model estimation and temporal contrastive learning.
result The approach learns representations that capture the underlying structure of the domain and improve sample efficiency.
Deep RL agents perform poorly on unseen states, challenging the notion of generalization.
problem Challenges in understanding and measuring generalization in deep reinforcement learning.
method Proposed definitions of generalization, practical evaluation methods, and demonstrations on a benchmark task.
result Deep Q-networks learn poor representations for states not selected on-policy, even if they are not adversarial.
POLAR learns efficient data acquisition policies using pretrained belief representations.
problem Challenges in learning effective policies for adaptive data acquisition.
method POLAR decouples representation learning from policy learning by leveraging pretrained predictive foundation models as belief-state encoders.
result POLAR outperforms state-of-the-art methods across diverse tasks while requiring fewer training samples.
On overfitting and asymptotic bias in batch reinforcement learning with partial observabilitystat.ML
The paper analyzes the tradeoff between bias and overfitting in reinforcement learning with partial observability.
problem Analyzing the tradeoff between asymptotic bias and overfitting in reinforcement learning with partial observability.
method Theoretical analysis and empirical illustration using truncated history of observations and function approximators.
result A smaller state representation decreases the risk of overfitting, but potentially increases asymptotic bias.
The paper studies properties of stated SL(n)-skein algebras and their centers.
problem Properties of stated SL(n)-skein algebras and their centers.
method Quantum trace maps and embeddings into quantum tori.
result Finitely generation and PI-degrees of centers of stated SL(n)-skein algebras.