SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

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Showing 8901–8925 of 15113 papers

TitleStatusHype
Multi-agent Battery Storage Management using MPC-based Reinforcement Learning—0
Towards robust and domain agnostic reinforcement learning competitions—0
XIRL: Cross-embodiment Inverse Reinforcement LearningCode0
The Power of Exploiter: Provable Multi-Agent RL in Large State Spaces—0
Correcting Momentum in Temporal Difference LearningCode0
Learning to Guide a Saturation-Based Theorem Prover—0
A Computational Model of Representation Learning in the Brain Cortex, Integrating Unsupervised and Reinforcement Learning—0
Explainable Artificial Intelligence (XAI) for Increasing User Trust in Deep Reinforcement Learning Driven Autonomous Systems—0
Average-Reward Reinforcement Learning with Trust Region Methods—0
Concave Utility Reinforcement Learning: the Mean-Field Game Viewpoint—0
Learning Combinatorial Node Labeling Algorithms—0
Entropy Regularized Reinforcement Learning Using Large Deviation TheoryCode0
Identifiability in inverse reinforcement learning—0
Learning without Knowing: Unobserved Context in Continuous Transfer Reinforcement Learning—0
DisTop: Discovering a Topological representation to learn diverse and rewarding skills—0
3D UAV Trajectory and Data Collection Optimisation via Deep Reinforcement Learning—0
Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Problems by Reinforcement Learning—0
Heuristic-Guided Reinforcement Learning—0
Learning Routines for Effective Off-Policy Reinforcement Learning—0
Reinforcement Learning for Assignment Problem with Time Constraints—0
Resource Allocation in Disaggregated Data Centre Systems with Reinforcement Learning—0
Robustifying Reinforcement Learning Policies with L_1 Adaptive Control—0
Detecting and Adapting to Novelty in Games—0
Cross-Trajectory Representation Learning for Zero-Shot Generalization in RLCode0
Be Considerate: Objectives, Side Effects, and Deciding How to Act—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified