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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 8076–8100 of 15113 papers

TitleStatusHype
Average-Reward Reinforcement Learning with Trust Region Methods—0
Explainable Artificial Intelligence (XAI) for Increasing User Trust in Deep Reinforcement Learning Driven Autonomous Systems—0
Learning without Knowing: Unobserved Context in Continuous Transfer Reinforcement Learning—0
Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement LearningCode1
DisTop: Discovering a Topological representation to learn diverse and rewarding skills—0
Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Problems by Reinforcement Learning—0
Efficient Continuous Control with Double Actors and Regularized CriticsCode1
Distributional Reinforcement Learning with Unconstrained Monotonic Neural NetworksCode1
3D UAV Trajectory and Data Collection Optimisation via Deep Reinforcement Learning—0
Control-Oriented Model-Based Reinforcement Learning with Implicit DifferentiationCode1
ScheduleNet: Learn to solve multi-agent scheduling problems with reinforcement learningCode1
MALib: A Parallel Framework for Population-based Multi-agent Reinforcement LearningCode1
Learning Routines for Effective Off-Policy Reinforcement Learning—0
Heuristic-Guided Reinforcement Learning—0
Same State, Different Task: Continual Reinforcement Learning without InterferenceCode1
Reinforcement Learning for Assignment Problem with Time Constraints—0
Resource Allocation in Disaggregated Data Centre Systems with Reinforcement Learning—0
Online reinforcement learning with sparse rewards through an active inference capsuleCode1
Model-agnostic and Scalable Counterfactual Explanations via Reinforcement LearningCode2
Differentiable Architecture Search for Reinforcement LearningCode1
Robustifying Reinforcement Learning Policies with L_1 Adaptive Control—0
Detecting and Adapting to Novelty in Games—0
Be Considerate: Objectives, Side Effects, and Deciding How to Act—0
Cross-Trajectory Representation Learning for Zero-Shot Generalization in RLCode0
Celebrating Diversity in Shared Multi-Agent Reinforcement LearningCode1
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Benchmark Results

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