Neural Architecture Search has shown potential to automate the design of neural networks. Deep Reinforcement Learning based agents can learn complex architectural patterns, as well as explore a vast and compositional search space. On the other hand, evolutionary algorithms offer higher sample efficiency, which is criti…
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48 results for “neural agents”
NeuroMAS treats multi-agent systems as neural networks for scalable, trainable coordination.
problem Designing multi-agent systems as hand-designed workflows is inefficient and inflexible.
method NeuroMAS treats multi-agent systems as a neural network architecture with reinforcement learning for scalable coordination.
result NeuroMAS improves significantly over multi-agent baselines and can be scaled progressively.
The Neural Testbed evaluates joint predictions of neural agents, revealing their limitations.
problem Evaluating the quality of joint predictions generated by neural agents.
method Developed an open-source benchmark (The Neural Testbed) to assess agents' marginal and joint predictions.
result Popular Bayesian deep learning agents perform poorly on joint predictions, even with accurate marginal predictions.
We present the MagNet, a neural network-based multi-agent interaction model to discover the governing dynamics and predict evolution of a complex multi-agent system from observations. We formulate a multi-agent system as a coupled non-linear network with a generic ordinary differential equation (ODE) based state evolut…
MPLP learns neural network weights by treating operations as message-passing agents.
problem Training neural networks using gradient-based methods.
method MPLP abstracts neural network operations as message-passing agents, updating internal states and passing messages.
result MPLP outperforms traditional gradient-based methods on simple feed-forward neural networks.
Iterative Model-Based Reinforcement Learning Using Simulations in the Differentiable Neural Computercs.LG
We propose a lifelong learning architecture, the Neural Computer Agent (NCA), where a Reinforcement Learning agent is paired with a predictive model of the environment learned by a Differentiable Neural Computer (DNC). The agent and DNC model are trained in conjunction iteratively. The agent improves its policy in simu…
Cross-dimensional neural networks improve AI in Catan game.
problem Challenging to build AI agents for Catan game using RL.
method Introduced cross-dimensional neural networks to handle game complexities.
result RL agent outperforms best heuristic agent in Catan.
One-shot path planning for multiple agents using neural networks.
problem Efficiently generating optimal or near-optimal paths for multiple agents in robotics.
method Utilizes fully convolutional neural networks for one-shot multi-agent path planning.
result Demonstrates successful generation of optimal or near-optimal paths in over 85% of cases for multi-path planning.
I2C enables agents to learn efficient communication without redundancy.
problem Redundant broadcast communication in multi-agent cooperation.
method I2C learns a prior for agent-agent communication via causal inference and reinforcement learning.
result I2C reduces communication overhead and improves multi-agent cooperative performance.
AgentNet is a graph neural network that learns to walk graphs intelligently, outperforming traditional methods.
problem Graph-level tasks, especially distinguishing and classifying graphs.
method AgentNet uses a computational model inspired by sublinear algorithms, where neural agents walk the graph and collectively decide the output.
result AgentNet can distinguish and separate graphs that are hard to distinguish, outperforming traditional graph neural networks.
Proposes a graph neural network for efficient multi-agent routing.
problem Routing multiple agents in complex, sparsely connected graphs with dynamic traffic.
method Value Iteration Network based on graph neural networks for online coordination.
result Significantly outperforms traditional methods in terms of cost and runtime.
Hybrid model simulates market dynamics using neural stochastic background traders.
problem Lack of realistic LOB simulations that combine historical data and dynamic interactions.
method Neural stochastic background trader trained on historical LOB data, embedded in multi-agent simulation.
result Hybrid model recreates stylised market facts and financial herding behaviors.
Improves neural relational inference for dynamic multi-agent trajectories.
problem Limited accuracy of NRI in short output sequences for relational inference in multi-agent trajectories.
method Proposes DYnamic multi-AgentRelational Inference (DYARI) model to handle changing interactions over time.
result DYARI model outperforms NRI in dynamic relational inference tasks.
Unified policy controls diverse agents through modular neural networks.
problem Learning control policies for various agent morphologies.
method Shared Modular Policies (SMP) with decentralized control and message passing.
result A single modular policy controls multiple agent morphologies.
Deep reinforcement learning methods attain super-human performance in a wide range of environments. Such methods are grossly inefficient, often taking orders of magnitudes more data than humans to achieve reasonable performance. We propose Neural Episodic Control: a deep reinforcement learning agent that is able to rap…
ReF-ER algorithm improved performance in multi-agent reinforcement learning.
problem Improving performance in multi-agent reinforcement learning environments.
method Extended ReF-ER algorithm to include dependencies between agents and modeled environment dynamics.
result ReF-ER MARL outperforms state-of-the-art algorithms in collaborative environments.
VectorNet predicts car behavior using vectorized HD maps and agent dynamics.
problem Predicting behavior in multi-agent systems with self-driving cars.
method VectorNet uses hierarchical graph neural networks on vectorized representations of HD maps and agent trajectories.
result VectorNet achieves comparable or better performance than state-of-the-art methods while using fewer parameters and less computational power.
Recently, deep reinforcement learning (RL) methods have been applied successfully to multi-agent scenarios. Typically, these methods rely on a concatenation of agent states to represent the information content required for decentralized decision making. However, concatenation scales poorly to swarm systems with a large…
Model predicts multi-agent trajectories using a differentiable simulator.
problem Predicting future positions of multiple interacting agents.
method Conditional recurrent variational neural networks (CVRNNs) with a kinematic bicycle model.
result Achieves state-of-the-art results on INTERACTION dataset.
New approach for open ad hoc teamwork using graph-based policy learning.
problem Designing autonomous agents to collaborate with changing teams without prior coordination.
method Graph-based policy learning to adapt to dynamic team compositions.
result Successfully models the effects of other agents, leading to robust adaptation and superior performance.
Agent-based modeling is a paradigm of modeling dynamic systems of interacting agents that are individually governed by specified behavioral rules. Training a model of such agents to produce an emergent behavior by specification of the emergent (as opposed to agent) behavior is easier from a demonstration perspective. W…
Reinforcement learning (RL) algorithms allow agents to learn skills and strategies to perform complex tasks without detailed instructions or expensive labelled training examples. That is, RL agents can learn, as we learn. Given the importance of learning in our intelligence, RL has been thought to be one of key compone…
Graph neural networks learn decentralized controllers from data.
problem Finding optimal decentralized controllers for autonomous agents is challenging.
method Adapting graph neural networks to handle delayed communications and ensure scalability and transferability.
result Graph neural networks can learn decentralized controllers from data, addressing the scalability and practical implementation issues of centralized controllers.
Residual U-net with Self-Attention to Solve Multi-Agent Time-Consistent Optimal Trade Executionq-fin.TR
Deep neural net solves multi-agent optimal trading problem.
problem Optimal trade execution for multiple agents and assets.
method Residual U-net with self-attention for viscosity solution approximation.
result Neural network approach outperforms finite difference methods.
Many potential applications of reinforcement learning in the real world involve interacting with other agents whose numbers vary over time. We propose new neural policy architectures for these multi-agent problems. In contrast to other methods of training an individual, discrete policy for each agent and then enforcing…
Deep active inference agents learn complex environments using Monte-Carlo methods.
problem Understanding and modeling biological intelligence in complex, continuous state-spaces.
method Neural architecture for deep active inference agents using multiple forms of Monte-Carlo sampling.
result Deep active inference agents can learn environmental dynamics and plan future actions.
Meta-agent learns effective exploration from offline data.
problem Design a meta-agent to quickly maximize reward in unseen tasks.
method Bayesian RL approach with adaptive neural belief estimate.
result Meta-agent learns effective exploration behavior from diverse tasks.
With the breakthrough of computational power and deep neural networks, many areas that we haven't explore with various techniques that was researched rigorously in past is feasible. In this paper, we will walk through possible concepts to achieve robo-like trading or advising. In order to accomplish similar level of pe…
We propose a new perspective on adversarial attacks against deep reinforcement learning agents. Our main contribution is CopyCAT, a targeted attack able to consistently lure an agent into following an outsider's policy. It is pre-computed, therefore fast inferred, and could thus be usable in a real-time scenario. We sh…
ISMCTS-BR learns best responses in large games, approximating worst-case performance.
problem Learning robustness to worst-case outcomes in large games.
method ISMCTS-BR, a scalable search-based algorithm for deep reinforcement learning.
result ISMCTS-BR approximates worst-case performance in large games.
We introduce two approaches for combining neural evolution strategy (NES) and proximal policy optimization (PPO): parameter transfer and parameter space noise. Parameter transfer is a PPO agent with parameters transferred from a NES agent. Parameter space noise is to directly add noise to the PPO agent`s parameters. We…
Fast risk assessment for autonomous vehicles using learned agent futures.
problem Risk assessment for autonomous vehicles given probabilistic predictions of other agents' futures.
method Non-sampling based methods using deep neural networks for probabilistic predictions, with Gaussian and non-Gaussian mixture models for agent positions and controls.
result Effective risk assessment for low probability events using learned models of agent futures.
A Neural Network-based method for unsupervised landmarks map estimation from measurements taken from landmarks is introduced. The measurements needed for training the network are the signals observed/received from landmarks by an agent. The definition of landmarks, agent, and the measurements taken by agent from landma…
We tackle the blackbox issue of deep neural networks in the settings of reinforcement learning (RL) where neural agents learn towards maximizing reward gains in an uncontrollable way. Such learning approach is risky when the interacting environment includes an expanse of state space because it is then almost impossible…
We propose a unified mechanism for achieving coordination and communication in Multi-Agent Reinforcement Learning (MARL), through rewarding agents for having causal influence over other agents' actions. Causal influence is assessed using counterfactual reasoning. At each timestep, an agent simulates alternate actions t…
Combining Pessimism with Optimism for Robust and Efficient Model-Based Deep Reinforcement Learningcs.LG
RH-UCRL combines pessimism and optimism for robust RL.
problem Ensuring reliable performance in real-world RL tasks with worst-case scenarios.
method RH-UCRL is a model-based RL algorithm that optimizes between an agent and an adversary, distinguishing between epistemic and aleatoric uncertainty.
result RH-UCRL achieves near-optimal sample complexity guarantees and outperforms other robust RL algorithms in adversarial environments.
We introduce a new RL problem where the agent is required to generalize to a previously-unseen environment characterized by a subtask graph which describes a set of subtasks and their dependencies. Unlike existing hierarchical multitask RL approaches that explicitly describe what the agent should do at a high level, ou…
Efficiently learns MFC systems with unknown dynamics.
problem Learning in multi-agent systems with non-stationary interactions and combinatorial state/action spaces.
method Model-based reinforcement learning algorithm, , balancing exploration and exploitation.
result First general regret bounds for model-based reinforcement learning of MFC systems.
Graph neural networks-based Scheduler for Production planning problems using Reinforcement Learningcs.LG
GraSP-RL uses graph neural networks to improve job shop scheduling.
problem Capturing machine-unit-job sequence relationships and managing state space growth.
method Graph neural networks for feature extraction, reinforcement learning for decision-making, decentralized optimization.
result GraSP-RL outperforms existing methods in minimizing makespan for complex production environments.
We explore neural painters, a generative model for brushstrokes learned from a real non-differentiable and non-deterministic painting program. We show that when training an agent to "paint" images using brushstrokes, using a differentiable neural painter leads to much faster convergence. We propose a method for encoura…
Emergence of Grid-like Representations by Training Recurrent Networks with Conformal Normalizationq-bio.NC
The paper studies how grid cell patterns emerge in neural networks.
problem Understanding how grid cells in the brain form hexagonal firing patterns.
method Training recurrent neural networks with conformal normalization of velocity inputs.
result Conformal normalization is crucial for the emergence of hexagonal grid patterns in neural networks.
Assisted by neural networks, reinforcement learning agents have been able to solve increasingly complex tasks over the last years. The simulation environment in which the agents interact is an essential component in any reinforcement learning problem. The environment simulates the dynamics of the agents' world and henc…
FEPS models agents to learn and act in complex environments without deep neural networks.
problem Modeling complex adaptive systems and understanding self-organizing behavior.
method Introducing Free Energy Projective Simulation (FEPS) within the constraints of the free energy principle and active inference.
result FEPS agents resolve ambiguity and infer optimal policies in partially observable environments.
We introduce gym-city, a Reinforcement Learning environment that uses SimCity 1's game engine to simulate an urban environment, wherein agents might seek to optimize one or a combination of any number of city-wide metrics, on gameboards of various sizes. We focus on population, and analyze our agents' ability to genera…
We propose a reinforcement learning agent to solve hard exploration games by learning a range of directed exploratory policies. We construct an episodic memory-based intrinsic reward using k-nearest neighbors over the agent's recent experience to train the directed exploratory policies, thereby encouraging the agent to…
Recently, prediction markets have shown considerable promise for developing flexible mechanisms for machine learning. In this paper, agents with isoelastic utilities are considered. It is shown that the costs associated with homogeneous markets of agents with isoelastic utilities produce equilibrium prices correspondin…
WayDCM predicts trajectories considering long-term goals, improving accuracy.
problem Predicting future trajectories of dynamic agents in complex environments.
method WayDCM combines DCM and NN to predict intermediate goals and trajectories, considering long-term goals.
result WayDCM outperforms previous methods on the Waymo Open dataset.
Approach to optimize bidding policies offline using reinforcement learning.
problem Optimizing spending in online advertising under budget constraints.
method Offline reinforcement learning for optimizing differentiable base policies.
result Statistically significant performance gains in production bidding environments.