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

Showing 24012450 of 15113 papers

TitleStatusHype
Bandit-Based Policy Invariant Explicit Shaping for Incorporating External Advice in Reinforcement Learning0
Adaptive Sampling Quasi-Newton Methods for Zeroth-Order Stochastic Optimization0
A comparison of controller architectures and learning mechanisms for arbitrary robot morphologies0
Continual and Multi-task Reinforcement Learning With Shared Episodic Memory0
Bandit approach to conflict-free multi-agent Q-learning in view of photonic implementation0
BANANAS: Bayesian Optimization with Neural Networks for Neural Architecture Search0
Adaptive Sampling Quasi-Newton Methods for Derivative-Free Stochastic Optimization0
BAMDP Shaping: a Unified Theoretical Framework for Intrinsic Motivation and Reward Shaping0
A Multifidelity Sim-to-Real Pipeline for Verifiable and Compositional Reinforcement Learning0
A Bibliometric Analysis and Review on Reinforcement Learning for Transportation Applications0
Contingency-constrained economic dispatch with safe reinforcement learning0
Balancing Two-Player Stochastic Games with Soft Q-Learning0
Adaptive Safe Reinforcement Learning-Enabled Optimization of Battery Fast-Charging Protocols0
A Comparison of Classical and Deep Reinforcement Learning Methods for HVAC Control0
Balancing SoC in Battery Cells using Safe Action Perturbations0
Balancing Reinforcement Learning Training Experiences in Interactive Information Retrieval0
A Multi-Document Coverage Reward for RELAXed Multi-Document Summarization0
Multiagent Model-based Credit Assignment for Continuous Control0
Continual Adversarial Reinforcement Learning (CARL) of False Data Injection detection: forgetting and explainability0
Continual Auxiliary Task Learning0
Balancing Progress and Safety: A Novel Risk-Aware Objective for RL in Autonomous Driving0
Balancing Profit, Risk, and Sustainability for Portfolio Management0
A Multi-Agent Reinforcement Learning Testbed for Cognitive Radio Applications0
Balancing Profit and Fairness in Risk-Based Pricing Markets0
Adaptive routing protocols for determining optimal paths in AI multi-agent systems: a priority- and learning-enhanced approach0
A Comparison of Action Spaces for Learning Manipulation Tasks0
Balancing Constraints and Rewards with Meta-Gradient D4PG0
Balancing Act: Prioritization Strategies for LLM-Designed Restless Bandit Rewards0
A Multi-Agent Reinforcement Learning Method for Impression Allocation in Online Display Advertising0
Balancing Accuracy and Fairness for Interactive Recommendation with Reinforcement Learning0
Balancing a CartPole System with Reinforcement Learning -- A Tutorial0
A Multi-agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learning0
Adaptive Rollout Length for Model-Based RL Using Model-Free Deep RL0
Contextual Transformer for Offline Meta Reinforcement Learning0
A Multiagent Reinforcement Learning Algorithm with Non-linear Dynamics0
Contextual Latent-Movements Off-Policy Optimization for Robotic Manipulation Skills0
A Comparative Study of Reinforcement Learning Techniques on Dialogue Management0
Bag of Policies for Distributional Deep Exploration0
No-regret Exploration in Contextual Reinforcement Learning0
Adaptive Road Configurations for Improved Autonomous Vehicle-Pedestrian Interactions using Reinforcement Learning0
Bad-Policy Density: A Measure of Reinforcement Learning Hardness0
AbFlowNet: Optimizing Antibody-Antigen Binding Energy via Diffusion-GFlowNet Fusion0
BadGPT: Exploring Security Vulnerabilities of ChatGPT via Backdoor Attacks to InstructGPT0
BADDr: Bayes-Adaptive Deep Dropout RL for POMDPs0
A Multi-Agent Deep Reinforcement Learning Coordination Framework for Connected and Automated Vehicles at Merging Roadways0
Contextual Policy Transfer in Reinforcement Learning Domains via Deep Mixtures-of-Experts0
Contingency-Aware Exploration in Reinforcement Learning0
A Multi-Agent Deep Reinforcement Learning Approach for a Distributed Energy Marketplace in Smart Grids0
Backward Imitation and Forward Reinforcement Learning via Bi-directional Model Rollouts0
Contextual Exploration Using a Linear Approximation Method Based on Satisficing0
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Benchmark Results

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