General Reinforcement Learning
35 papers with code • 6 benchmarks • 7 datasets
Benchmarks
These leaderboards are used to track progress in General Reinforcement Learning
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Libraries
Use these libraries to find General Reinforcement Learning models and implementations
ProcGen
Obstacle Tower
WLD
PackIt
Avalon
bipedal-skills
POPGym
Latest papers
Discovering General Reinforcement Learning Algorithms with Adversarial Environment Design
EmptyJackson/groove
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NeurIPS 2023
Recently, it has been shown that it is possible to meta-learn update rules, with the hope of discovering algorithms that can perform well on a wide range of RL tasks.
Learning to Backdoor Federated Learning
In particular, we propose a general reinforcement learning-based backdoor attack framework where the attacker first trains a (non-myopic) attack policy using a simulator built upon its local data and common knowledge on the FL system, which is then applied during actual FL training.
DeFIX: Detecting and Fixing Failure Scenarios with Reinforcement Learning in Imitation Learning Based Autonomous Driving
In this paper, we present a Reinforcement Learning (RL) based methodology to DEtect and FIX (DeFIX) failures of an Imitation Learning (IL) agent by extracting infraction spots and re-constructing mini-scenarios on these infraction areas to train an RL agent for fixing the shortcomings of the IL approach.
Intelligent Resource Allocation in Joint Radar-Communication With Graph Neural Networks
joleeson/Directional-JRC
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IEEE Transactions on Vehicular Technology 2022
In this paper, we propose a framework for intelligent vehicles to conduct JRC, with minimal prior knowledge of the system model and a tunable performance balance, in an environment where surrounding vehicles execute radar detection periodically, which is typical in contemporary protocols.
Learning Deformable Object Manipulation from Expert Demonstrations
uscresl/dmfd
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We test DMfD on a set of representative manipulation tasks for a 1-dimensional rope and a 2-dimensional cloth from the SoftGym suite of tasks, each with state and image observations.
Doubly-Robust Estimation for Correcting Position-Bias in Click Feedback for Unbiased Learning to Rank
harrieo/2022-doubly-robust-ltr
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The prevalent approach to unbiased click-based learning-to-rank (LTR) is based on counterfactual inverse-propensity-scoring (IPS) estimation.
Intelligent Trading Systems: A Sentiment-Aware Reinforcement Learning Approach
The feasibility of making profitable trades on a single asset on stock exchanges based on patterns identification has long attracted researchers.
Catastrophic Interference in Reinforcement Learning: A Solution Based on Context Division and Knowledge Distillation
sweety-dm/interference-aware-deep-q-learning
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In this paper, we present IQ, i. e., interference-aware deep Q-learning, to mitigate catastrophic interference in single-task deep reinforcement learning.
QKSA: Quantum Knowledge Seeking Agent
In this article we present the motivation and the core thesis towards the implementation of a Quantum Knowledge Seeking Agent (QKSA).
Adaptive Rational Activations to Boost Deep Reinforcement Learning
ml-research/rational_activations
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Latest insights from biology show that intelligence not only emerges from the connections between neurons but that individual neurons shoulder more computational responsibility than previously anticipated.
