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Found 24 papers, 8 papers with code
One is More: Diverse Perspectives within a Single Network for Efficient DRL
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
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.
Probabilistic Generative Modeling for Procedural Roundabout Generation for Developing Countries
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
GFlowNets are probabilistic models that learn a stochastic policy that sequentially generates compositional structures, such as molecular graphs.
Beyond Conservatism: Diffusion Policies in Offline Multi-agent Reinforcement Learning
We present a novel Diffusion Offline Multi-agent Model (DOM2) for offline Multi-Agent Reinforcement Learning (MARL).
Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets
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.
Stochastic Generative Flow Networks
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
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.
Better Training of GFlowNets with Local Credit and Incomplete Trajectories
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
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
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
Multi-user delay constrained scheduling is important in many real-world applications including wireless communication, live streaming, and cloud computing.
RLx2: Training a Sparse Deep Reinforcement Learning Model from Scratch
Training deep reinforcement learning (DRL) models usually requires high computation costs.
Network Topology Optimization via Deep Reinforcement Learning
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.
Regularized Softmax Deep Multi-Agent Q-Learning
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.
Plan Better Amid Conservatism: Offline Multi-Agent Reinforcement Learning with Actor Rectification
Conservatism has led to significant progress in offline reinforcement learning (RL) where an agent learns from pre-collected datasets.
Regularized Softmax Deep Multi-Agent $Q$-Learning
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.
Softmax Deep Double Deterministic Policy Gradients
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.
Multi-Path Policy Optimization
Recent years have witnessed a tremendous improvement of deep reinforcement learning.
Deterministic Value-Policy Gradients
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.
Reinforcement Learning with Dynamic Boltzmann Softmax Updates
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.
A Convergent Variant of the Boltzmann Softmax Operator in Reinforcement Learning
We then propose the dynamic Boltzmann softmax(DBS) operator to enable the convergence to the optimal value function in value iteration.
Deterministic Policy Gradients With General State Transitions
Such a setting generalizes the widely-studied stochastic state transition setting, namely the setting of deterministic policy gradient (DPG).
A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems
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.
