Papers rllib
“rllib” 태그가 달린 논문 23편 · 필터 해제
SocialJax: An Evaluation Suite for Multi-agent Reinforcement Learning in Sequential Social Dilemmas
Sequential social dilemmas pose a significant challenge in the field of multi-agent reinforcement learning (MARL), requiring environments that accurately reflect the tension between individual and collective interests. P…
Multi-agent Reinforcement Learningreinforcement-learningReinforcement LearningrllibHighly Parallelized Reinforcement Learning Training with Relaxed Assignment Dependencies
As the demands for superior agents grow, the training complexity of Deep Reinforcement Learning (DRL) becomes higher. Thus, accelerating training of DRL has become a major research focus. Dividing the DRL training proces…
Deep Reinforcement Learningreinforcement-learningReinforcement LearningrllibDeep Reinforcement Learning for Dynamic Resource Allocation in Wireless Networks
This report investigates the application of deep reinforcement learning (DRL) algorithms for dynamic resource allocation in wireless communication systems. An environment that includes a base station, multiple antennas, …
Deep Reinforcement LearningrllibSchedulingA Multi-Agent Reinforcement Learning Testbed for Cognitive Radio Applications
Technological trends show that Radio Frequency Reinforcement Learning (RFRL) will play a prominent role in the wireless communication systems of the future. Applications of RFRL range from military communications jamming…
Multi-agent Reinforcement LearningOpenAI Gymreinforcement-learningReinforcement Learning+2Wireless MAC Protocol Synthesis and Optimization with Multi-Agent Distributed Reinforcement Learning
In this letter, we propose a novel Multi-Agent Deep Reinforcement Learning (MADRL) framework for Medium Access Control (MAC) protocol design. Unlike centralized approaches, which rely on a single entity for decision-maki…
Decision MakingDeep Reinforcement LearningrllibScalable Volt-VAR Optimization using RLlib-IMPALA Framework: A Reinforcement Learning Approach
In the rapidly evolving domain of electrical power systems, the Volt-VAR optimization (VVO) is increasingly critical, especially with the burgeoning integration of renewable energy sources. Traditional approaches to lear…
Deep Reinforcement LearningDistributed Computingreinforcement-learningReinforcement Learning+1LExCI: A Framework for Reinforcement Learning with Embedded Systems
Advances in artificial intelligence (AI) have led to its application in many areas of everyday life. In the context of control engineering, reinforcement learning (RL) represents a particularly promising approach as it i…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)rllibgym-saturation: Gymnasium environments for saturation provers (System description)
This work describes a new version of a previously published Python package - gym-saturation: a collection of OpenAI Gym environments for guiding saturation-style provers based on the given clause algorithm with reinforce…
OpenAI Gymreinforcement-learningReinforcement Learningrllib+1MolOpt: Autonomous Molecular Geometry Optimization using Multi-Agent Reinforcement Learning
In this paper, we propose MolOpt, the first attempt of its kind to use Multi-Agent Reinforcement Learning (MARL) for autonomous molecular geometry optimization (MGO). Typically MGO algorithms are hand-designed, but MolOp…
3D geometryComputational chemistryMolecular geometry optimizationMulti-agent Reinforcement Learning+5IxDRL: A Novel Explainable Deep Reinforcement Learning Toolkit based on Analyses of Interestingness
In recent years, advances in deep learning have resulted in a plethora of successes in the use of reinforcement learning (RL) to solve complex sequential decision tasks with high-dimensional inputs. However, existing sys…
Deep Reinforcement LearningReinforcement Learning (RL)rllibPOPGym: Benchmarking Partially Observable Reinforcement Learning
Real world applications of Reinforcement Learning (RL) are often partially observable, thus requiring memory. Despite this, partial observability is still largely ignored by contemporary RL benchmarks and libraries. We i…
BenchmarkingGPUPartially Observable Reinforcement Learningreinforcement-learning+4CoRL: Environment Creation and Management Focused on System Integration
Existing reinforcement learning environment libraries use monolithic environment classes, provide shallow methods for altering agent observation and action spaces, and/or are tied to a specific simulation environment. Th…
Managementreinforcement-learningReinforcement LearningReinforcement Learning (RL)+1RayNet: A Simulation Platform for Developing Reinforcement Learning-Driven Network Protocols
Reinforcement Learning (RL) has gained significant momentum in the development of network protocols. However, RL-based protocols are still in their infancy, and substantial research is required to build deployable soluti…
reinforcement-learningReinforcement Learning (RL)rllibLamarckian Platform: Pushing the Boundaries of Evolutionary Reinforcement Learning towards Asynchronous Commercial Games
Despite the emerging progress of integrating evolutionary computation into reinforcement learning, the absence of a high-performance platform endowing composability and massive parallelism causes non-trivial difficulties…
CPUDistributed Computingreinforcement-learningReinforcement Learning+2Project proposal: A modular reinforcement learning based automated theorem prover
We propose to build a reinforcement learning prover of independent components: a deductive system (an environment), the proof state representation (how an agent sees the environment), and an agent training algorithm. To …
OpenAI Gymreinforcement-learningReinforcement LearningReinforcement Learning (RL)+1VMAS: A Vectorized Multi-Agent Simulator for Collective Robot Learning
While many multi-robot coordination problems can be solved optimally by exact algorithms, solutions are often not scalable in the number of robots. Multi-Agent Reinforcement Learning (MARL) is gaining increasing attentio…
BenchmarkingMulti-agent Reinforcement LearningrllibElegantRL-Podracer: Scalable and Elastic Library for Cloud-Native Deep Reinforcement Learning
Deep reinforcement learning (DRL) has revolutionized learning and actuation in applications such as game playing and robotic control. The cost of data collection, i.e., generating transitions from agent-environment inter…
Deep Reinforcement LearningGPUreinforcement-learningReinforcement Learning+3Godot Reinforcement Learning Agents
We present Godot Reinforcement Learning (RL) Agents, an open-source interface for developing environments and agents in the Godot Game Engine. The Godot RL Agents interface allows the design, creation and learning of age…
CPUreinforcement-learningReinforcement LearningReinforcement Learning (RL)+1FinRL-Podracer: High Performance and Scalable Deep Reinforcement Learning for Quantitative Finance
Machine learning techniques are playing more and more important roles in finance market investment. However, finance quantitative modeling with conventional supervised learning approaches has a number of limitations. The…
Deep Reinforcement LearningGPUreinforcement-learningReinforcement Learning+3MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning
Population-based multi-agent reinforcement learning (PB-MARL) refers to the series of methods nested with reinforcement learning (RL) algorithms, which produces a self-generated sequence of tasks arising from the coupled…
Atari GamesCPUDistributed ComputingMulti-agent Reinforcement Learning+3