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 7551–7600 of 15113 papers

TitleStatusHype
Automated Reinforcement Learning: An Overview—0
Dyna-T: Dyna-Q and Upper Confidence Bounds Applied to Trees—0
The Recurrent Reinforcement Learning Crypto Agent—0
Toddler-Guidance Learning: Impacts of Critical Period on Multimodal AI Agents—0
Multi-echelon Supply Chains with Uncertain Seasonal Demands and Lead Times Using Deep Reinforcement Learning—0
Task Independent Capsule-Based Agents for Deep Q-Learning—0
Pavlovian Signalling with General Value Functions in Agent-Agent Temporal Decision Making—0
STIR^2: Reward Relabelling for combined Reinforcement and Imitation Learning on sparse-reward tasks—0
Active Reinforcement Learning -- A Roadmap Towards Curious Classifier Systems for Self-Adaptation—0
Benchmarking Deep Reinforcement Learning Algorithms for Vision-based Robotics—0
Automated Reinforcement Learning (AutoRL): A Survey and Open Problems—0
Distributed Cooperative Multi-Agent Reinforcement Learning with Directed Coordination Graph—0
Opportunities of Hybrid Model-based Reinforcement Learning for Cell Therapy Manufacturing Process Control—0
State of the Art of User Simulation approaches for conversational information retrieval—0
When is Offline Two-Player Zero-Sum Markov Game Solvable?—0
A Multi-agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learning—0
Assessing Policy, Loss and Planning Combinations in Reinforcement Learning using a New Modular Architecture—0
Neural Network Optimization for Reinforcement Learning Tasks Using Sparse Computations—0
Offline Reinforcement Learning for Road Traffic Control—0
Combining Reinforcement Learning and Inverse Reinforcement Learning for Asset Allocation Recommendations—0
Offsetting Unequal Competition through RL-assisted Incentive Schemes—0
Deep Learning-based Predictive Control of Battery Management for Frequency RegulationCode0
Learning Complex Spatial Behaviours in ABM: An Experimental Observational Study—0
Deep Reinforcement Learning, a textbook—0
Analyzing Micro-Founded General Equilibrium Models with Many Agents using Deep Reinforcement Learning—0
Execute Order 66: Targeted Data Poisoning for Reinforcement Learning—0
A Deeper Understanding of State-Based Critics in Multi-Agent Reinforcement Learning—0
Actor-Critic Network for Q&A in an Adversarial Environment—0
Toward Causal-Aware RL: State-Wise Action-Refined Temporal DifferenceCode0
Robust Algorithmic Collusion—0
Reinforcement Learning for Task Specifications with Action-Constraints—0
Temporal Complementarity-Guided Reinforcement Learning for Image-to-Video Person Re-Identification—0
Transfer RL across Observation Feature Spaces via Model-Based Regularization—0
Operator Deep Q-Learning: Zero-Shot Reward Transferring in Reinforcement Learning—0
Toward Pareto Efficient Fairness-Utility Trade-off inRecommendation through Reinforcement Learning—0
Symmetry-Aware Neural Architecture for Embodied Visual Exploration—0
Joint Learning-Based Stabilization of Multiple Unknown Linear Systems—0
A Surrogate-Assisted Controller for Expensive Evolutionary Reinforcement Learning—0
Importance of Empirical Sample Complexity Analysis for Offline Reinforcement Learning—0
Stochastic convex optimization for provably efficient apprenticeship learning—0
A Theoretical Understanding of Gradient Bias in Meta-Reinforcement LearningCode0
Using Graph-Aware Reinforcement Learning to Identify Winning Strategies in Diplomacy Games (Student Abstract)—0
Single-Shot Pruning for Offline Reinforcement Learning—0
Robust Entropy-regularized Markov Decision Processes—0
Stability-Preserving Automatic Tuning of PID Control with Reinforcement Learning—0
Multi-Agent Reinforcement Learning via Adaptive Kalman Temporal Difference and Successor Representation—0
Reversible Upper Confidence Bound Algorithm to Generate Diverse Optimized Candidates—0
MORAL: Aligning AI with Human Norms through Multi-Objective Reinforced Active LearningCode0
Constructing a Good Behavior Basis for Transfer using Generalized Policy Updates—0
Constraint Sampling Reinforcement Learning: Incorporating Expertise For Faster LearningCode0
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Benchmark Results

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