Search Results for author: Ling Pan

Found 24 papers, 8 papers with code

One is More: Diverse Perspectives within a Single Network for Efficient DRL

no code implementations21 Oct 2023 Yiqin Tan, Ling Pan, Longbo Huang

Deep reinforcement learning has achieved remarkable performance in various domains by leveraging deep neural networks for approximating value functions and policies.


Pre-Training and Fine-Tuning Generative Flow Networks

no code implementations5 Oct 2023 Ling Pan, Moksh Jain, Kanika Madan, Yoshua Bengio

However, as they are typically trained from a given extrinsic reward function, it remains an important open challenge about how to leverage the power of pre-training and train GFlowNets in an unsupervised fashion for efficient adaptation to downstream tasks.

Unsupervised Pre-training

Probabilistic Generative Modeling for Procedural Roundabout Generation for Developing Countries

no code implementations5 Oct 2023 Zarif Ikram, Ling Pan, Dianbo Liu

Due to limited resources and fast economic growth, designing optimal transportation road networks with traffic simulation and validation in a cost-effective manner is vital for developing countries, where extensive manual testing is expensive and often infeasible.

Learning to Scale Logits for Temperature-Conditional GFlowNets

no code implementations4 Oct 2023 Minsu Kim, Joohwan Ko, Dinghuai Zhang, Ling Pan, Taeyoung Yun, Woochang Kim, Jinkyoo Park, Yoshua Bengio

GFlowNets are probabilistic models that learn a stochastic policy that sequentially generates compositional structures, such as molecular graphs.

Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets

1 code implementation26 May 2023 Dinghuai Zhang, Hanjun Dai, Nikolay Malkin, Aaron Courville, Yoshua Bengio, Ling Pan

In this paper, we design Markov decision processes (MDPs) for different combinatorial problems and propose to train conditional GFlowNets to sample from the solution space.

Combinatorial Optimization

Stochastic Generative Flow Networks

1 code implementation19 Feb 2023 Ling Pan, Dinghuai Zhang, Moksh Jain, Longbo Huang, Yoshua Bengio

Generative Flow Networks (or GFlowNets for short) are a family of probabilistic agents that learn to sample complex combinatorial structures through the lens of "inference as control".

Distributional GFlowNets with Quantile Flows

no code implementations11 Feb 2023 Dinghuai Zhang, Ling Pan, Ricky T. Q. Chen, Aaron Courville, Yoshua Bengio

Generative Flow Networks (GFlowNets) are a new family of probabilistic samplers where an agent learns a stochastic policy for generating complex combinatorial structure through a series of decision-making steps.

Decision Making

Better Training of GFlowNets with Local Credit and Incomplete Trajectories

1 code implementation3 Feb 2023 Ling Pan, Nikolay Malkin, Dinghuai Zhang, Yoshua Bengio

Generative Flow Networks or GFlowNets are related to Monte-Carlo Markov chain methods (as they sample from a distribution specified by an energy function), reinforcement learning (as they learn a policy to sample composed objects through a sequence of steps), generative models (as they learn to represent and sample from a distribution) and amortized variational methods (as they can be used to learn to approximate and sample from an otherwise intractable posterior, given a prior and a likelihood).

E-MAPP: Efficient Multi-Agent Reinforcement Learning with Parallel Program Guidance

no code implementations5 Dec 2022 Can Chang, Ni Mu, Jiajun Wu, Ling Pan, Huazhe Xu

Specifically, we introduce Efficient Multi-Agent Reinforcement Learning with Parallel Program Guidance(E-MAPP), a novel framework that leverages parallel programs to guide multiple agents to efficiently accomplish goals that require planning over $10+$ stages.

Multi-agent Reinforcement Learning reinforcement-learning +1

Generative Augmented Flow Networks

no code implementations7 Oct 2022 Ling Pan, Dinghuai Zhang, Aaron Courville, Longbo Huang, Yoshua Bengio

We specify intermediate rewards by intrinsic motivation to tackle the exploration problem in sparse reward environments.

Effective Multi-User Delay-Constrained Scheduling with Deep Recurrent Reinforcement Learning

1 code implementation30 Aug 2022 Pihe Hu, Ling Pan, Yu Chen, Zhixuan Fang, Longbo Huang

Multi-user delay constrained scheduling is important in many real-world applications including wireless communication, live streaming, and cloud computing.

Cloud Computing reinforcement-learning +2

Network Topology Optimization via Deep Reinforcement Learning

no code implementations19 Apr 2022 Zhuoran Li, Xing Wang, Ling Pan, Lin Zhu, Zhendong Wang, Junlan Feng, Chao Deng, Longbo Huang

A2C-GS consists of three novel components, including a verifier to validate the correctness of a generated network topology, a graph neural network (GNN) to efficiently approximate topology rating, and a DRL actor layer to conduct a topology search.

Management reinforcement-learning +1

Regularized Softmax Deep Multi-Agent Q-Learning

1 code implementation NeurIPS 2021 Ling Pan, Tabish Rashid, Bei Peng, Longbo Huang, Shimon Whiteson

Tackling overestimation in $Q$-learning is an important problem that has been extensively studied in single-agent reinforcement learning, but has received comparatively little attention in the multi-agent setting.

Multi-agent Reinforcement Learning Q-Learning +4

Plan Better Amid Conservatism: Offline Multi-Agent Reinforcement Learning with Actor Rectification

no code implementations22 Nov 2021 Ling Pan, Longbo Huang, Tengyu Ma, Huazhe Xu

Conservatism has led to significant progress in offline reinforcement learning (RL) where an agent learns from pre-collected datasets.

Continuous Control Multi-agent Reinforcement Learning +3

Regularized Softmax Deep Multi-Agent $Q$-Learning

no code implementations22 Mar 2021 Ling Pan, Tabish Rashid, Bei Peng, Longbo Huang, Shimon Whiteson

Tackling overestimation in $Q$-learning is an important problem that has been extensively studied in single-agent reinforcement learning, but has received comparatively little attention in the multi-agent setting.

Multi-agent Reinforcement Learning Q-Learning +4

Softmax Deep Double Deterministic Policy Gradients

1 code implementation NeurIPS 2020 Ling Pan, Qingpeng Cai, Longbo Huang

A widely-used actor-critic reinforcement learning algorithm for continuous control, Deep Deterministic Policy Gradients (DDPG), suffers from the overestimation problem, which can negatively affect the performance.

Continuous Control

Multi-Path Policy Optimization

no code implementations11 Nov 2019 Ling Pan, Qingpeng Cai, Longbo Huang

Recent years have witnessed a tremendous improvement of deep reinforcement learning.

Efficient Exploration

Deterministic Value-Policy Gradients

no code implementations9 Sep 2019 Qingpeng Cai, Ling Pan, Pingzhong Tang

Based on this theoretical guarantee, we propose a class of the deterministic value gradient algorithm (DVG) with infinite horizon, and different rollout steps of the analytical gradients by the learned model trade off between the variance of the value gradients and the model bias.

Continuous Control reinforcement-learning +1

Reinforcement Learning with Dynamic Boltzmann Softmax Updates

1 code implementation14 Mar 2019 Ling Pan, Qingpeng Cai, Qi Meng, Wei Chen, Longbo Huang, Tie-Yan Liu

In this paper, we propose to update the value function with dynamic Boltzmann softmax (DBS) operator, which has good convergence property in the setting of planning and learning.

Atari Games Q-Learning +2

A Convergent Variant of the Boltzmann Softmax Operator in Reinforcement Learning

no code implementations27 Sep 2018 Ling Pan, Qingpeng Cai, Qi Meng, Wei Chen, Tie-Yan Liu

We then propose the dynamic Boltzmann softmax(DBS) operator to enable the convergence to the optimal value function in value iteration.

Atari Games Q-Learning +2

Deterministic Policy Gradients With General State Transitions

no code implementations10 Jul 2018 Qingpeng Cai, Ling Pan, Pingzhong Tang

Such a setting generalizes the widely-studied stochastic state transition setting, namely the setting of deterministic policy gradient (DPG).

Continuous Control

A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems

no code implementations13 Feb 2018 Ling Pan, Qingpeng Cai, Zhixuan Fang, Pingzhong Tang, Longbo Huang

Different from existing methods that often ignore spatial information and rely heavily on accurate prediction, HRP captures both spatial and temporal dependencies using a divide-and-conquer structure with an embedded localized module.

reinforcement-learning Reinforcement Learning (RL)

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