Efficiently explores concurrent RL agents for practical scale problems.
problem Efficiently exploring multiple RL agents in a shared environment.
method Combines seed sampling and randomized value function learning.
result Approach is competitive and learns quickly with fewer agents.
Paper proposes a method to learn and exceed expert demonstrations in unknown reward environments.
problem Learning to outperform expert demonstrations in unknown reward environments.
method A novel concurrent reward and action policy learning approach with a stereo utility definition.
result The proposed method can outperform expert demonstrations in various environments.
Novel framework for data sharing and coordinated exploration in concurrent RL with non-identical environments.
problem Learning more data-efficient and better policies in concurrent RL with non-identical environments.
method Proposes a novel algorithmic framework that leverages causal inference via ANM-MM to extract model parameters and a new data sharing scheme based on similarity measures.
result Demonstrates superior learning speeds on various tasks and effectiveness of diverse action selection.
Improved RL for 3D games with concurrent actions.
problem Training complex behaviors in 3D games.
method Deep reinforcement learning architecture with concurrent actions.
result 4x improvement in training time and 2.5x in performance.
A framework for multi-agent learning improves coordination through a memory-driven communication protocol.
problem Coordination and synchronisation in multi-agent systems with limited observations.
method A memory-driven communication protocol learned concurrently with individual policies during training.
result Superior performance in small-scale systems with up to six agents, demonstrating improved coordination.
Algorithm improves multi-agent learning with noisy observations.
problem Challenges in learning optimal policies with noisy, weakly correlated observations.
method Enhanced multi-agent deep deterministic policy gradient algorithm (MADDPG-M) with a communication medium.
result Algorithm performs well in complex, non-stationary environments, offering significant performance gains.
This paper surveys methods to handle non-stationarity in multi-agent deep reinforcement learning.
problem Non-stationarity in multi-agent reinforcement learning environments.
method Modifications in training procedures, opponent policy representation learning, meta-learning, communication, and decentralized learning.
result A comprehensive review of recent works on addressing non-stationarity in multi-agent deep reinforcement learning.
Improved PAC guarantees for multi-agent reinforcement learning with noisy communication.
problem Improving exploration in cooperative multi-agent reinforcement learning with communication constraints.
method Develops PAC guarantees for multiple concurrent MDPs with noisy and resource-limited communication.
result Theoretical and empirical improvements in sample complexity for information fusion.
Agent learns from an expert, adapting to constraints in concept learning.
problem Insufficient query selection in active learning for realistic human domains.
method Imitation learning to reason about both internal goals and external constraints.
result Agent outperforms other active learners under most constrained conditions.
New algorithms optimize decision rules in strategic scenarios, minimizing prediction risk and incentivizing better outcomes.
problem Strategic agents manipulate features to improve outcomes, complicating decision-making models.
method Efficient algorithms for learning decision rules that minimize prediction risk, incentivize better outcomes, and estimate true model coefficients.
result Optimal decision rules can be learned through testing and observing agent responses, circumventing hardness results.
Dynamic sentiment analysis improves stock trading strategies.
problem Improving stock trading performance through dynamic agent switching.
method Combining sentiment analysis with deep reinforcement learning and dynamic agent switching.
result Dynamic sentiment-based trading strategy outperforms traditional methods.
A deep RL approach learns multi-agent coordination through dynamic graph communication.
problem Learning collaborative policies in multi-agent systems.
method Connectivity Driven Communication (CDC) approach using graph-based attention mechanisms.
result CDC learns effective collaborative policies and outperforms other methods in cooperative navigation tasks.
A deep RL framework optimizes resource allocation in wireless networks.
problem Optimizing resource allocation and interference in wireless networks.
method Multi-agent deep reinforcement learning for distributed decision-making.
result Our approach outperforms decentralized and centralized baselines in terms of user rates.
GUM tackles MARL by avoiding overestimation through state-marginal restriction.
problem Overestimation of values in large joint state-action spaces.
method Greedy UnMixing through state-marginal restriction and unmixing.
result Superior performance compared to existing Q-learning and general MARL algorithms.
The paper defines semi-concurrent vector fields on Finsler manifolds and explores their implications.
problem Characterizing Finsler manifolds with semi-concurrent vector fields.
method Introducing and investigating semi-concurrent vector fields, proving properties, and providing examples.
result Finsler manifolds admitting semi-concurrent vector fields are either Riemannian or conic Finslerian.
This paper examines a generalized Kropina metric and its geometric properties.
problem Investigating geometric properties of a generalized Kropina metric.
method Analyzing a Finsler manifold with a concurrent π-vector field and examining the φ-concurrent generalized Kropina change. result The geodesic sprays of the original Finsler metric and the modified metric are never projectively related.
Efficient actor-critic learning with shared experience replay improves data efficiency.
problem Challenges in actor-critic reinforcement learning with experience replay and off-policy learning stability.
method Combining actor-critic algorithms with shared experience replay, analyzing V-trace, proposing a trust region scheme.
result State-of-the-art data efficiency on Atari achieved with 200M environment frames.
The paper studies Finsler spaces with semi-concurrent vector fields and their equivalence to Riemannian spaces.
problem Characterizing Finsler spaces with semi-concurrent vector fields.
method Analyzing various Finsler spaces and proving conditions for equivalence to Riemannian spaces.
result Various Finsler spaces (quasi-C-reducible, C3-like, Ch-recurrent, P2-like) are equivalent to Riemannian spaces if they admit a semi-concurrent vector field. A Ricci soliton (Mn,g,v,λ) on a Riemannian manifold (Mn,g) is said to have concurrent potential field if its potential field v is a concurrent vector field. In the first part of this paper we completely classify Ricci solitons with concurrent potential fields. In the second part we derive a necessary and suffic…
AGENTICAITA uses AI agents to autonomously trade markets without human intervention.
problem Inability of traditional trading systems to adapt to market complexity.
method Introduces an agentic AI framework with specialized LLM agents reasoning, negotiating, and acting.
result Demonstrated operational correctness and non-trivial inter-agent negotiation in live market conditions.
Agents learn to play a first-person multiplayer game at human level performance.
problem Training AI agents for complex, multi-agent, real-time environments.
method Population-based deep reinforcement learning with concurrent training of multiple agents.
result Achieved human-level performance in a first-person multiplayer game.
A Ricci soliton (M,g,v,λ) on a Riemannian manifold (M,g) is said to have concurrent potential field if its potential field v is a concurrent vector field. Ricci solitons arisen from concurrent vector fields on Riemannian manifolds were studied recently in \cite{CD2}. The most important concurrent vector field is …
A new method improves ridesharing efficiency using QMIX.
problem Improving ridesharing dispatch efficiency with complex environments.
method QMIX for centralized training with decentralized execution.
result QMIX outperforms IDQN in various scenarios.
Study intrinsic motivation for synergistic tasks in reinforcement learning.
problem Sparse-reward synergistic tasks where multiple agents must work together.
method Propose incentivizing actions that affect the world in ways not achievable individually, using either true states or a dynamics model.
result Our approach yields more efficient learning than typical methods.
MERL uses evolutionary and gradient-based methods to optimize sparse team-based and dense agent-specific rewards in multiagent coordination.
problem Training multiagent reinforcement learning policies on sparse team-based rewards is difficult and relying solely on agent-specific rewards is sub-optimal.
method MERL employs a split-level training platform with an evolutionary algorithm and a gradient-based optimizer, transferring skills between the two processes.
result MERL significantly outperforms state-of-the-art methods on coordination benchmarks.
ATT-MADDPG models teammates' policies in RL for cooperative systems.
problem Modeling and exploiting changing policies of teammates in cooperative multi-agent systems.
method Extends DDPG with a centralized critic and attention mechanism to model dynamic joint policies.
result ATT-MADDPG outperforms state-of-the-art RL methods and achieves better scalability and robustness.
Improved gap-dependent bounds for reinforcement learning with linear approximations.
problem Achieving nearly minimax-optimal performance with linear function approximation.
method Developed and analyzed the LSVI-UCB++ algorithm and its concurrent variant.
result First gap-dependent regret bound for nearly minimax-optimal algorithm LSVI-UCB++.
Speeds up deep neural networks training by 10x using GPU concurrency.
problem Training deep residual neural networks efficiently.
method Layer-wise parallel training with GPU concurrency and Nonlinear Multigrid.
result 10.2x speedup over traditional techniques.
This work improves privacy in federated combinatorial bandits by balancing regret and privacy.
problem Privacy-preserving learning in competitive online learning settings with quality constraints.
method Proposes P-FCB algorithm for federated combinatorial bandits, balancing regret and privacy.
result Improves regret while maintaining quality constraints and privacy guarantees.
A distributed RL framework optimizes radio resource management for wireless networks.
problem Interference in wireless networks limits performance; maximizing average and worst-case throughput is challenging.
method Multi-agent deep reinforcement learning (RL) for distributed link scheduling.
result The framework achieves superior average and 5th percentile user throughput compared to decentralized methods.
This paper improves neural network training performance by optimizing concurrency and operation scheduling.
problem Managing and scheduling fine-grained operations in neural network training for high performance.
method Extending TensorFlow runtime to enable automatic concurrency control and scheduling, using performance modeling.
result Achieved 33% average performance improvement on neural network models, up to 49%.
The paper classifies almost Yamabe solitons on hypersurfaces and submanifolds in Euclidean spaces.
problem Classifying almost Yamabe solitons on various geometric structures.
method Analyzing hypersurfaces and submanifolds with position and concurrent vector fields.
result Complete classification of almost Yamabe solitons on hypersurfaces and submanifolds.
BCO* improves BCO by concurrently training inverse dynamics and expert policy.
problem Efficiently learn from unlabeled demonstrations without requiring many initial interactions.
method Introduce BCO* that concurrently trains an inverse dynamics model and expert policy.
result BCO* eliminates the need for initial interactions and improves sample complexity.
A new reinforcement learning method for robots thinking and moving simultaneously.
problem Concurrent control in robotic systems where actions must be decided while the system is still evolving.
method Continuous-time Bellman equations, discretization aware of system delays, and architectural extension to deep reinforcement learning.
result The method successfully handles tasks requiring simultaneous decision-making and action execution.
PolySwarm uses a swarm of LLMs to predict and arbitrage prediction markets.
problem Real-time prediction market trading and latency arbitrage inefficiencies.
method PolySwarm employs a swarm of 50 diverse LLMs, Bayesian combination, and risk-controlled execution.
result Swarm aggregation outperforms single-model baselines in prediction tasks.
The present paper deals with an \emph{intrinsic} investigation of the notion of a concurrent π-vector field on the pullback bundle of a Finsler manifold (M,L). The effect of the existence of a concurrent π-vector field on some important special Finsler spaces is studied. An intrinsic investigation of a particular…
New algorithm provably converges to second-order stationary points in NMF.
problem Understanding convergence to local minima in NMF.
method Multiplicative weight update dynamics, concurrent updates, and simplex reduction.
result Provable convergence to second-order stationary points.
We consider the problem of concurrent portfolio losses in two non-overlapping credit portfolios. In order to explore the full statistical dependence structure of such portfolio losses, we estimate their empirical pairwise copulas. Instead of a Gaussian dependence, we typically find a strong asymmetry in the copulas. Co…
Study herding in finance using particle model.
problem Understanding collective behavior in financial markets.
method Agent-based particle model for non-coordinated interactions and dynamic market signals.
result Gronwall and LaSalle type estimates for herding behavior.
New approach uses unlabeled prior data to accelerate exploration in sparse reward tasks.
problem Sparse reward tasks in reinforcement learning.
method Learn reward model from online experience, label prior data, and use concurrently.
result Rapid exploration in challenging sparse-reward domains.
PODNet discovers plannable options from unstructured demonstrations.
problem Learning from unstructured, multi-objective demonstrations.
method Custom categorical variational autoencoder, recurrent option inference network, option-conditioned policy network, and option dynamics model.
result PODNet enables learning from demonstration for multiple tasks and planning.
We generalize Matsumoto metrics with a special π-form and explore their geometric properties.
problem Exploring the geometric properties of generalized Matsumoto metrics with a special π-form.
method Considering a Finsler manifold with a concurrent π-vector field, we introduce a change in the metric and analyze its geometric properties.
result The generalized φ-Matsumoto metric can never be projectively related to the original metric.
Safe RL approach using Lyapunov functions.
problem Concurrent optimization of performance and safety constraints in RL.
method Lyapunov-based approach for CMDPs, transforming DP and RL algorithms.
result Significant performance improvement in balancing constraints and performance.
Deep learning model classifies concurrent human interactions from WiFi data with high accuracy.
problem Classifying concurrent human interactions from WiFi data with high accuracy.
method Attention-BiGRU deep learning model using Multiple Input Multiple Output radio link.
result Maximum benchmark accuracy of 94% for a single subject-pair, 88% for ten subject pairs.
New algorithm trains neural nets on simple skills to learn complex tasks faster.
problem Learning complex tasks through simple imitation.
method Train neural networks on simple, easy-to-learn skills to accelerate learning of complex, hard-to-learn tasks.
result Consistently outperforms state-of-the-art baseline in training speed and performance.
A novel method optimizes variable-stiffness structures for better strength and weight.
problem Optimizing variable-stiffness structures for higher strength and lighter weight.
method A novel multi-stage concurrent topology optimization scheme combining DMO, S-BPTO, and CFAO.
result The method ensures better fibre angle convergence and stable optimization.
We study the equilibrium positions of three points on a convex curve under influence of the Coulomb potential. We identify these positions as orthotripods, three points on the curve having concurrent normals. This relates the equilibrium positions to the caustic (evolute) of the curve. The concurrent normals can only m…
Modeling long-range context for multi-function utterances in dialogues.
problem Complex dependencies across dialogue turns in long utterances.
method Adapted Convolutional Recurrent Neural Network (CRNN) to model interactions between utterances.
result Significantly outperforms existing work on CDA recognition on a tech forum dataset.