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 5451–5500 of 15113 papers

TitleStatusHype
Beyond Exponentially Fast Mixing in Average-Reward Reinforcement Learning via Multi-Level Monte Carlo Actor-Critic—0
Constrained Policy Optimization with Explicit Behavior Density for Offline Reinforcement LearningCode0
A Memory Efficient Deep Reinforcement Learning Approach For Snake Game Autonomous Agents—0
Generalized Munchausen Reinforcement Learning using Tsallis KL Divergence—0
Exploring Deep Reinforcement Learning for Holistic Smart Building Control—0
Improving Behavioural Cloning with Positive Unlabeled Learning—0
Reinforcement Learning from Diverse Human Preferences—0
Solving Richly Constrained Reinforcement Learning through State Augmentation and Reward Penalties—0
Modeling human road crossing decisions as reward maximization with visual perception limitations—0
Single-Trajectory Distributionally Robust Reinforcement Learning—0
SNeRL: Semantic-aware Neural Radiance Fields for Reinforcement Learning—0
Which Experiences Are Influential for Your Agent? Policy Iteration with Turn-over DropoutCode0
Trajectory-Aware Eligibility Traces for Off-Policy Reinforcement LearningCode0
Principled Reinforcement Learning with Human Feedback from Pairwise or K-wise Comparisons—0
Model-based Offline Reinforcement Learning with Local Misspecification—0
Certifiably Robust Reinforcement Learning through Model-Based Abstract Interpretation—0
On the Global Convergence of Risk-Averse Policy Gradient Methods with Expected Conditional Risk Measures—0
Learning from Multiple Independent Advisors in Multi-agent Reinforcement LearningCode0
Learning to Generate All Feasible Actions—0
FedHQL: Federated Heterogeneous Q-Learning—0
Communication-Efficient Collaborative Regret Minimization in Multi-Armed Bandits—0
A Deep Neural Network Algorithm for Linear-Quadratic Portfolio Optimization with MGARCH and Small Transaction Costs—0
ASQ-IT: Interactive Explanations for Reinforcement-Learning Agents—0
Explainable Deep Reinforcement Learning: State of the Art and Challenges—0
Autonomous particles—0
Constrained Reinforcement Learning for Dexterous ManipulationCode0
Intrinsic Motivation in Model-based Reinforcement Learning: A Brief Review—0
AutoCost: Evolving Intrinsic Cost for Zero-violation Reinforcement Learning—0
A Novel Deep Reinforcement Learning-based Approach for Enhancing Spectral Efficiency of IRS-assisted Wireless Systems—0
SMART: Self-supervised Multi-task pretrAining with contRol Transformers—0
Minimal Value-Equivalent Partial Models for Scalable and Robust Planning in Lifelong Reinforcement Learning—0
Story Shaping: Teaching Agents Human-like Behavior with Stories—0
Model Based Reinforcement Learning with Non-Gaussian Environment Dynamics and its Application to Portfolio Optimization—0
Learning to View: Decision Transformers for Active Object Detection—0
Forecaster-aided User Association and Load Balancing in Multi-band Mobile Networks—0
Quasi-optimal Reinforcement Learning with Continuous Actions—0
The configurable tree graph (CT-graph): measurable problems in partially observable and distal reward environments for lifelong reinforcement learningCode0
Revisiting Estimation Bias in Policy Gradients for Deep Reinforcement Learning—0
Multi-Armed Bandits and Quantum Channel Oracles—0
Multi-agent Reinforcement Learning with Graph Q-Networks for Antenna Tuning—0
Modeling Moral Choices in Social Dilemmas with Multi-Agent Reinforcement LearningCode0
Reinforcement learning-based estimation for partial differential equations—0
Asynchronous Deep Double Duelling Q-Learning for Trading-Signal Execution in Limit Order Book Markets—0
Generative Slate Recommendation with Reinforcement Learning—0
Domain-adapted Learning and Imitation: DRL for Power Arbitrage—0
A Survey of Meta-Reinforcement Learning—0
Generalization through Diversity: Improving Unsupervised Environment Design—0
Advanced Scaling Methods for VNF deployment with Reinforcement Learning—0
Domain-adapted Learning and Interpretability: DRL for Gas Trading—0
Tight Guarantees for Interactive Decision Making with the Decision-Estimation Coefficient—0
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Benchmark Results

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