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Found 116 papers, 51 papers with code
Sequence to Sequence Reward Modeling: Improving RLHF by Language Feedback
Reinforcement learning from human feedback (RLHF) aligns LLMs by training a reward model (RM) on human preferences and fine-tuning the LLMs to maximize RM feedback.
A Survey on Self-play Methods in Reinforcement Learning
Self-play, characterized by agents' interactions with copies or past versions of itself, has recently gained prominence in reinforcement learning.
ProgressGym: Alignment with a Millennium of Moral Progress
Frontier AI systems, including large language models (LLMs), hold increasing influence over the epistemology of human users.
SafeSora: Towards Safety Alignment of Text2Video Generation via a Human Preference Dataset
To mitigate the risk of harmful outputs from large vision models (LVMs), we introduce the SafeSora dataset to promote research on aligning text-to-video generation with human values.
PKU-SafeRLHF: A Safety Alignment Preference Dataset for Llama Family Models
In this work, we introduce the PKU-SafeRLHF dataset, designed to promote research on safety alignment in large language models (LLMs).
In-Context Editing: Learning Knowledge from Self-Induced Distributions
The existing fine-tuning paradigm for language models is brittle in knowledge editing scenarios, where the model must incorporate new information without extensive retraining.
Emerging Safety Attack and Defense in Federated Instruction Tuning of Large Language Models
Federated learning (FL) enables multiple parties to collaboratively fine-tune an large language model (LLM) without the need of direct data sharing.
Efficient Adaptation in Mixed-Motive Environments via Hierarchical Opponent Modeling and Planning
To address these issues, we propose Hierarchical Opponent modeling and Planning (HOP), a novel multi-agent decision-making algorithm that enables few-shot adaptation to unseen policies in mixed-motive environments.
Language Models Resist Alignment
Empirically, we demonstrate the elasticity of post-alignment models, i. e., the tendency to revert to the behavior distribution formed during the pre-training phase upon further fine-tuning.
Efficient Preference-based Reinforcement Learning via Aligned Experience Estimation
To boost the learning loop, we propose SEER, an efficient PbRL method that integrates label smoothing and policy regularization techniques.
Efficient Model-agnostic Alignment via Bayesian Persuasion
This paper explores an efficient method for aligning black-box large models using smaller models, introducing a model-agnostic and lightweight Bayesian Persuasion Alignment framework.
End-to-End Neuro-Symbolic Reinforcement Learning with Textual Explanations
Neuro-symbolic reinforcement learning (NS-RL) has emerged as a promising paradigm for explainable decision-making, characterized by the interpretability of symbolic policies.
AnySkill: Learning Open-Vocabulary Physical Skill for Interactive Agents
Traditional approaches in physics-based motion generation, centered around imitation learning and reward shaping, often struggle to adapt to new scenarios.
Incentive Compatibility for AI Alignment in Sociotechnical Systems: Positions and Prospects
The burgeoning integration of artificial intelligence (AI) into human society brings forth significant implications for societal governance and safety.
Reward Generalization in RLHF: A Topological Perspective
As a solution, we introduce a theoretical framework for investigating reward generalization in reinforcement learning from human feedback (RLHF), focusing on the topology of information flow at both macro and micro levels.
Aligner: Efficient Alignment by Learning to Correct
However, the tension between the complexity of current alignment methods and the need for rapid iteration in deployment scenarios necessitates the development of a model-agnostic alignment approach that can operate under these constraints.
Panacea: Pareto Alignment via Preference Adaptation for LLMs
Panacea trains a single model capable of adapting online and Pareto-optimally to diverse sets of preferences without the need for further tuning.
CivRealm: A Learning and Reasoning Odyssey in Civilization for Decision-Making Agents
Within CivRealm, we provide interfaces for two typical agent types: tensor-based agents that focus on learning, and language-based agents that emphasize reasoning.
A Perspective of Q-value Estimation on Offline-to-Online Reinforcement Learning
In this paper, from a novel perspective, we systematically study the challenges that remain in O2O RL and identify that the reason behind the slow improvement of the performance and the instability of online finetuning lies in the inaccurate Q-value estimation inherited from offline pretraining.
JARVIS-1: Open-World Multi-task Agents with Memory-Augmented Multimodal Language Models
Achieving human-like planning and control with multimodal observations in an open world is a key milestone for more functional generalist agents.
AI Alignment: A Comprehensive Survey
The former aims to make AI systems aligned via alignment training, while the latter aims to gain evidence about the systems' alignment and govern them appropriately to avoid exacerbating misalignment risks.
Grasp Multiple Objects with One Hand
The intricate kinematics of the human hand enable simultaneous grasping and manipulation of multiple objects, essential for tasks such as object transfer and in-hand manipulation.
Safe RLHF: Safe Reinforcement Learning from Human Feedback
However, the inherent tension between the objectives of helpfulness and harmlessness presents a significant challenge during LLM training.
Safety-Gymnasium: A Unified Safe Reinforcement Learning Benchmark
By introducing this benchmark, we aim to facilitate the evaluation and comparison of safety performance, thus fostering the development of reinforcement learning for safer, more reliable, and responsible real-world applications.
MaskMA: Towards Zero-Shot Multi-Agent Decision Making with Mask-Based Collaborative Learning
Building a single generalist agent with strong zero-shot capability has recently sparked significant advancements.
Robust Multi-Agent Reinforcement Learning by Mutual Information Regularization
Existing robust MARL methods either approximate or enumerate all possible threat scenarios against worst-case adversaries, leading to computational intensity and reduced robustness.
GEAR: A GPU-Centric Experience Replay System for Large Reinforcement Learning Models
This paper introduces a distributed, GPU-centric experience replay system, GEAR, designed to perform scalable reinforcement learning (RL) with large sequence models (such as transformers).
Evolving Diverse Red-team Language Models in Multi-round Multi-agent Games
Furthermore, we develop a Gamified Red Team Solver (GRTS) with diversity measures to mitigate mode collapse and theoretically guarantee the convergence of approximate Nash equilibrium which results in better strategies for both teams.
ValueDCG: Measuring Comprehensive Human Value Understanding Ability of Language Models
Personal values are a crucial factor behind human decision-making.
Mixup-Augmented Meta-Learning for Sample-Efficient Fine-Tuning of Protein Simulators
Molecular dynamics simulations have emerged as a fundamental instrument for studying biomolecules.
ProAgent: Building Proactive Cooperative Agents with Large Language Models
Building agents with adaptive behavior in cooperative tasks stands as a paramount goal in the realm of multi-agent systems.
JiangJun: Mastering Xiangqi by Tackling Non-Transitivity in Two-Player Zero-Sum Games
This paper presents an empirical exploration of non-transitivity in perfect-information games, specifically focusing on Xiangqi, a traditional Chinese board game comparable in game-tree complexity to chess and shogi.
Theoretically Guaranteed Policy Improvement Distilled from Model-Based Planning
Model-based reinforcement learning (RL) has demonstrated remarkable successes on a range of continuous control tasks due to its high sample efficiency.
SafeDreamer: Safe Reinforcement Learning with World Models
Existing Safe Reinforcement Learning (SafeRL) methods, which rely on cost functions to enforce safety, often fail to achieve zero-cost performance in complex scenarios, especially vision-only tasks.
BeaverTails: Towards Improved Safety Alignment of LLM via a Human-Preference Dataset
In this paper, we introduce the BeaverTails dataset, aimed at fostering research on safety alignment in large language models (LLMs).
Large Sequence Models for Sequential Decision-Making: A Survey
Transformer architectures have facilitated the development of large-scale and general-purpose sequence models for prediction tasks in natural language processing and computer vision, e. g., GPT-3 and Swin Transformer.
Maximum Entropy Heterogeneous-Agent Reinforcement Learning
We embed cooperative MARL problems into probabilistic graphical models, from which we derive the maximum entropy (MaxEnt) objective for MARL.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Deep Reinforcement Learning with Task-Adaptive Retrieval via Hypernetwork
Deep reinforcement learning algorithms are usually impeded by sampling inefficiency, heavily depending on multiple interactions with the environment to acquire accurate decision-making capabilities.
Heterogeneous Value Alignment Evaluation for Large Language Models
We conduct evaluations with new auto-metric \textit{value rationality} to represent the ability of LLMs to align with specific values.
OmniSafe: An Infrastructure for Accelerating Safe Reinforcement Learning Research
AI systems empowered by reinforcement learning (RL) algorithms harbor the immense potential to catalyze societal advancement, yet their deployment is often impeded by significant safety concerns.
Heterogeneous-Agent Reinforcement Learning
The necessity for cooperation among intelligent machines has popularised cooperative multi-agent reinforcement learning (MARL) in AI research.
STAS: Spatial-Temporal Return Decomposition for Multi-agent Reinforcement Learning
It first decomposes the global return back to each time step, then utilizes the Shapley Value to redistribute the individual payoff from the decomposed global reward.
UniDexGrasp++: Improving Dexterous Grasping Policy Learning via Geometry-aware Curriculum and Iterative Generalist-Specialist Learning
We propose a novel, object-agnostic method for learning a universal policy for dexterous object grasping from realistic point cloud observations and proprioceptive information under a table-top setting, namely UniDexGrasp++.
DPPMask: Masked Image Modeling with Determinantal Point Processes
In particular, we evaluate our method on two representative MIM frameworks, MAE and iBOT.
ASP: Learn a Universal Neural Solver!
Applying machine learning to combinatorial optimization problems has the potential to improve both efficiency and accuracy.
Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority Influence
To achieve maximum deviation in victim policies under complex agent-wise interactions, our unilateral attack aims to characterize and maximize the impact of the adversary on the victims.
ACE: Cooperative Multi-agent Q-learning with Bidirectional Action-Dependency
In the learning phase, each agent minimizes the TD error that is dependent on how the subsequent agents have reacted to their chosen action.
Ranked #1 on
SMAC
on SMAC 3s5z_vs_3s6z
Contextual Transformer for Offline Meta Reinforcement Learning
Firstly, we propose prompt tuning for offline RL, where a context vector sequence is concatenated with the input to guide the conditional policy generation.
TorchOpt: An Efficient Library for Differentiable Optimization
TorchOpt further provides a high-performance distributed execution runtime.
MARLlib: A Scalable and Efficient Multi-agent Reinforcement Learning Library
A significant challenge facing researchers in the area of multi-agent reinforcement learning (MARL) pertains to the identification of a library that can offer fast and compatible development for multi-agent tasks and algorithm combinations, while obviating the need to consider compatibility issues.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
GenDexGrasp: Generalizable Dexterous Grasping
By leveraging the contact map as a hand-agnostic intermediate representation, GenDexGrasp efficiently generates diverse and plausible grasping poses with a high success rate and can transfer among diverse multi-fingered robotic hands.
MSRL: Distributed Reinforcement Learning with Dataflow Fragments
Yet, current distributed RL systems tie the definition of RL algorithms to their distributed execution: they hard-code particular distribution strategies and only accelerate specific parts of the computation (e. g. policy network updates) on GPU workers.
End-to-End Affordance Learning for Robotic Manipulation
Such contact prediction process then leads to an end-to-end affordance learning framework that can generalize over different types of manipulation tasks.
Constrained Update Projection Approach to Safe Policy Optimization
Compared to previous safe RL methods, CUP enjoys the benefits of 1) CUP generalizes the surrogate functions to generalized advantage estimator (GAE), leading to strong empirical performance.
Debias the Black-box: A Fair Ranking Framework via Knowledge Distillation
Deep neural networks can capture the intricate interaction history information between queries and documents, because of their many complicated nonlinear units, allowing them to provide correct search recommendations.
Heterogeneous-Agent Mirror Learning: A Continuum of Solutions to Cooperative MARL
The necessity for cooperation among intelligent machines has popularised cooperative multi-agent reinforcement learning (MARL) in the artificial intelligence (AI) research community.
Scalable Model-based Policy Optimization for Decentralized Networked Systems
Reinforcement learning algorithms require a large amount of samples; this often limits their real-world applications on even simple tasks.
Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement Learning
In this study, we propose the Bimanual Dexterous Hands Benchmark (Bi-DexHands), a simulator that involves two dexterous hands with tens of bimanual manipulation tasks and thousands of target objects.
A Game-Theoretic Framework for Managing Risk in Multi-Agent Systems
Although there are equilibrium concepts in game theory that take into account risk aversion, they either assume that agents are risk-neutral with respect to the uncertainty caused by the actions of other agents, or they are not guaranteed to exist.
Multi-Agent Reinforcement Learning is a Sequence Modeling Problem
In this paper, we introduce a novel architecture named Multi-Agent Transformer (MAT) that effectively casts cooperative multi-agent reinforcement learning (MARL) into SM problems wherein the task is to map agents' observation sequence to agents' optimal action sequence.
On the Convergence of Fictitious Play: A Decomposition Approach
Fictitious play (FP) is one of the most fundamental game-theoretical learning frameworks for computing Nash equilibrium in $n$-player games, which builds the foundation for modern multi-agent learning algorithms.
Breaking the Curse of Dimensionality in Multiagent State Space: A Unified Agent Permutation Framework
To break this curse, we propose a unified agent permutation framework that exploits the permutation invariance (PI) and permutation equivariance (PE) inductive biases to reduce the multiagent state space.
Settling the Communication Complexity for Distributed Offline Reinforcement Learning
We study a novel setting in offline reinforcement learning (RL) where a number of distributed machines jointly cooperate to solve the problem but only one single round of communication is allowed and there is a budget constraint on the total number of information (in terms of bits) that each machine can send out.
Understanding Value Decomposition Algorithms in Deep Cooperative Multi-Agent Reinforcement Learning
Value function decomposition is becoming a popular rule of thumb for scaling up multi-agent reinforcement learning (MARL) in cooperative games.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Efficient Policy Space Response Oracles
Policy Space Response Oracle methods (PSRO) provide a general solution to learn Nash equilibrium in two-player zero-sum games but suffer from two drawbacks: (1) the computation inefficiency due to the need for consistent meta-game evaluation via simulations, and (2) the exploration inefficiency due to finding the best response against a fixed meta-strategy at every epoch.
A Theoretical Understanding of Gradient Bias in Meta-Reinforcement Learning
Gradient-based Meta-RL (GMRL) refers to methods that maintain two-level optimisation procedures wherein the outer-loop meta-learner guides the inner-loop gradient-based reinforcement learner to achieve fast adaptations.
Offline Pre-trained Multi-Agent Decision Transformer: One Big Sequence Model Tackles All SMAC Tasks
In this paper, we facilitate the research by providing large-scale datasets, and use them to examine the usage of the Decision Transformer in the context of MARL.
Towards Unifying Behavioral and Response Diversity for Open-ended Learning in Zero-sum Games
With this unified diversity measure, we design the corresponding diversity-promoting objective and population effectivity when seeking the best responses in open-ended learning.
Neural Auto-Curricula in Two-Player Zero-Sum Games
When solving two-player zero-sum games, multi-agent reinforcement learning (MARL) algorithms often create populations of agents where, at each iteration, a new agent is discovered as the best response to a mixture over the opponent population.
Multi-agent Reinforcement Learning
Vocal Bursts Valence Prediction
A Game-Theoretic Approach for Improving Generalization Ability of TSP Solvers
In this paper, we introduce a two-player zero-sum framework between a trainable \emph{Solver} and a \emph{Data Generator} to improve the generalization ability of deep learning-based solvers for Traveling Salesman Problem (TSP).
DESTA: A Framework for Safe Reinforcement Learning with Markov Games of Intervention
In this paper, we introduce a new generation of RL solvers that learn to minimise safety violations while maximising the task reward to the extent that can be tolerated by the safe policy.
Measuring the Non-Transitivity in Chess
In this paper, we quantify the non-transitivity in Chess through real-world data from human players.
Online Markov Decision Processes with Non-oblivious Strategic Adversary
In this setting, we first demonstrate that MDP-Expert, an existing algorithm that works well with oblivious adversaries can still apply and achieve a policy regret bound of $\mathcal{O}(\sqrt{T \log(L)}+\tau^2\sqrt{ T \log(|A|)})$ where $L$ is the size of adversary's pure strategy set and $|A|$ denotes the size of agent's action space.
Multi-Agent Constrained Policy Optimisation
To fill these gaps, in this work, we formulate the safe MARL problem as a constrained Markov game and solve it with policy optimisation methods.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Offline Pre-trained Multi-Agent Decision Transformer
Offline reinforcement learning leverages static datasets to learn optimal policies with no necessity to access the environment.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning
In this paper, we extend the theory of trust region learning to MARL.
Revisiting the Characteristics of Stochastic Gradient Noise and Dynamics
In this paper, we characterize the noise of stochastic gradients and analyze the noise-induced dynamics during training deep neural networks by gradient-based optimizers.
On the Complexity of Computing Markov Perfect Equilibrium in General-Sum Stochastic Games
Similar to the role of Markov decision processes in reinforcement learning, Stochastic Games (SGs) lay the foundation for the study of multi-agent reinforcement learning (MARL) and sequential agent interactions.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Settling the Variance of Multi-Agent Policy Gradients
In multi-agent RL (MARL), although the PG theorem can be naturally extended, the effectiveness of multi-agent PG (MAPG) methods degrades as the variance of gradient estimates increases rapidly with the number of agents.
Is Nash Equilibrium Approximator Learnable?
In this paper, we investigate the learnability of the function approximator that approximates Nash equilibrium (NE) for games generated from a distribution.
A Game-Theoretic Approach to Multi-Agent Trust Region Optimization
Trust region methods are widely applied in single-agent reinforcement learning problems due to their monotonic performance-improvement guarantee at every iteration.
Unifying Behavioral and Response Diversity for Open-ended Learning in Zero-sum Games
With this unified diversity measure, we design the corresponding diversity-promoting objective and population effectivity when seeking the best responses in open-ended learning.
MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning
Our framework is comprised of three key components: (1) a centralized task dispatching model, which supports the self-generated tasks and scalable training with heterogeneous policy combinations; (2) a programming architecture named Actor-Evaluator-Learner, which achieves high parallelism for both training and sampling, and meets the evaluation requirement of auto-curriculum learning; (3) a higher-level abstraction of MARL training paradigms, which enables efficient code reuse and flexible deployments on different distributed computing paradigms.
Neural Auto-Curricula
When solving two-player zero-sum games, multi-agent reinforcement learning (MARL) algorithms often create populations of agents where, at each iteration, a new agent is discovered as the best response to a mixture over the opponent population.
Cooperative Multi-Agent Transfer Learning with Level-Adaptive Credit Assignment
In addition, we use a novel agent network named Population Invariant agent with Transformer (PIT) to realize the coordination transfer in more varieties of scenarios.
Learning to Safely Exploit a Non-Stationary Opponent
On the other hand, overfitting to an opponent (i. e., exploiting only one specific type of opponent) makes the learning player easily exploitable by others.
Learning to Shape Rewards using a Game of Two Partners
Reward shaping (RS) is a powerful method in reinforcement learning (RL) for overcoming the problem of sparse or uninformative rewards.
Modelling Behavioural Diversity for Learning in Open-Ended Games
Promoting behavioural diversity is critical for solving games with non-transitive dynamics where strategic cycles exist, and there is no consistent winner (e. g., Rock-Paper-Scissors).
Online Double Oracle
Solving strategic games with huge action space is a critical yet under-explored topic in economics, operations research and artificial intelligence.
Foresee then Evaluate: Decomposing Value Estimation with Latent Future Prediction
Value function is the central notion of Reinforcement Learning (RL).
Diverse Auto-Curriculum is Critical for Successful Real-World Multiagent Learning Systems
Multiagent reinforcement learning (MARL) has achieved a remarkable amount of success in solving various types of video games.
Multi-Agent Trust Region Learning
We derive the lower bound of agents' payoff improvements for MATRL methods, and also prove the convergence of our method on the meta-game fixed points.
Robust Multi-Agent Reinforcement Learning Driven by Correlated Equilibrium
Therefore, to achieve robust CMARL, we introduce novel strategies to encourage agents to learn correlated equilibrium while maximally preserving the convenience of the decentralized execution.
Replica-Exchange Nos\'e-Hoover Dynamics for Bayesian Learning on Large Datasets
In this paper, we present a new practical method for Bayesian learning that can rapidly draw representative samples from complex posterior distributions with multiple isolated modes in the presence of mini-batch noise.
An Overview of Multi-Agent Reinforcement Learning from Game Theoretical Perspective
In this work, we provide a monograph on MARL that covers both the fundamentals and the latest developments in the research frontier.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving
We open-source the SMARTS platform and the associated benchmark tasks and evaluation metrics to encourage and empower research on multi-agent learning for autonomous driving.
What About Inputing Policy in Value Function: Policy Representation and Policy-extended Value Function Approximator
We study Policy-extended Value Function Approximator (PeVFA) in Reinforcement Learning (RL), which extends conventional value function approximator (VFA) to take as input not only the state (and action) but also an explicit policy representation.
What About Taking Policy as Input of Value Function: Policy-extended Value Function Approximator
The value function lies in the heart of Reinforcement Learning (RL), which defines the long-term evaluation of a policy in a given state.
Learning to Infer User Hidden States for Online Sequential Advertising
To drive purchase in online advertising, it is of the advertiser's great interest to optimize the sequential advertising strategy whose performance and interpretability are both important.
Multi-Agent Determinantal Q-Learning
Though practical, current methods rely on restrictive assumptions to decompose the centralized value function across agents for execution.
$α^α$-Rank: Practically Scaling $α$-Rank through Stochastic Optimisation
Furthermore, we also show successful results on large joint strategy profiles with a maximum size in the order of $\mathcal{O}(2^{25})$ ($\approx 33$ million joint strategies) -- a setting not evaluable using $\alpha$-Rank with reasonable computational budget.
Bi-level Actor-Critic for Multi-agent Coordination
Coordination is one of the essential problems in multi-agent systems.
Multiagent Systems
Spectral-based Graph Convolutional Network for Directed Graphs
Our approach can work directly on directed graph data in semi-supervised nodes classification tasks.
Replica-exchange Nosé-Hoover dynamics for Bayesian learning on large datasets
In this paper, we present a new practical method for Bayesian learning that can rapidly draw representative samples from complex posterior distributions with multiple isolated modes in the presence of mini-batch noise.
Disentangling Dynamics and Returns: Value Function Decomposition with Future Prediction
Value functions are crucial for model-free Reinforcement Learning (RL) to obtain a policy implicitly or guide the policy updates.
Modelling Bounded Rationality in Multi-Agent Interactions by Generalized Recursive Reasoning
Though limited in real-world decision making, most multi-agent reinforcement learning (MARL) models assume perfectly rational agents -- a property hardly met due to individual's cognitive limitation and/or the tractability of the decision problem.
Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning
Our methods are tested on both the matrix game and the differential game, which have a non-trivial equilibrium where common gradient-based methods fail to converge.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Can Deep Learning Predict Risky Retail Investors? A Case Study in Financial Risk Behavior Forecasting
The results of employing a deep network for operational risk forecasting confirm the feature learning capability of deep learning, provide guidance on designing a suitable network architecture and demonstrate the superiority of deep learning over machine learning and rule-based benchmarks.
Benchmarking Deep Sequential Models on Volatility Predictions for Financial Time Series
As an indicator of the level of risk or the degree of variation, volatility is important to analyse the financial market, and it is taken into consideration in various decision-making processes in financial activities.
Factorized Q-Learning for Large-Scale Multi-Agent Systems
Deep Q-learning has achieved a significant success in single-agent decision making tasks.
Multiagent Systems
Mean Field Multi-Agent Reinforcement Learning
Existing multi-agent reinforcement learning methods are limited typically to a small number of agents.
Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo for Bayesian learning
We propose a new sampling method, the thermostat-assisted continuously-tempered Hamiltonian Monte Carlo, for Bayesian learning on large datasets and multimodal distributions.
A Study of AI Population Dynamics with Million-agent Reinforcement Learning
We conduct an empirical study on discovering the ordered collective dynamics obtained by a population of intelligence agents, driven by million-agent reinforcement learning.
Adversarial Variational Bayes Methods for Tweedie Compound Poisson Mixed Models
Mixed models with random effects account for the covariance structure related to the grouping hierarchy in the data.
Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games
Many artificial intelligence (AI) applications often require multiple intelligent agents to work in a collaborative effort.
