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 1480114850 of 15113 papers

TitleStatusHype
Is Vanilla Policy Gradient Overlooked? Analyzing Deep Reinforcement Learning for HanabiCode0
Benchmarking Reinforcement Learning Algorithms on Real-World RobotsCode0
Gradual Transition from Bellman Optimality Operator to Bellman Operator in Online Reinforcement LearningCode0
Learn to Steer through Deep Reinforcement LearningCode0
An Efficient Application of Neuroevolution for Competitive Multiagent LearningCode0
Grammars and reinforcement learning for molecule optimizationCode0
Improving Policy Learning via Language Dynamics DistillationCode0
Correcting Momentum in Temporal Difference LearningCode0
Corpus-Level End-to-End Exploration for Interactive SystemsCode0
Improving Policy Optimization with Generalist-Specialist LearningCode0
CO-PILOT: COllaborative Planning and reInforcement Learning On sub-Task curriculumCode0
Improving Portfolio Optimization Results with Bandit NetworksCode0
Improving Post-Processing of Audio Event Detectors Using Reinforcement LearningCode0
COPA: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning AttacksCode0
Cooperative multi-agent reinforcement learning for high-dimensional nonequilibrium controlCode0
Graph Backup: Data Efficient Backup Exploiting Markovian TransitionsCode0
Environment Design for Inverse Reinforcement LearningCode0
Improving reinforcement learning algorithms: towards optimal learning rate policiesCode0
Improving Reinforcement Learning Based Image Captioning with Natural Language PriorCode0
Environment Probing Interaction PoliciesCode0
RH-Net: Improving Neural Relation Extraction via Reinforcement Learning and Hierarchical Relational SearchingCode0
Environments for Lifelong Reinforcement LearningCode0
Benchmarking Quantum Reinforcement LearningCode0
Benchmarking MOEAs for solving continuous multi-objective RL problemsCode0
Cooperative Inverse Reinforcement LearningCode0
Improving Generalization in Reinforcement Learning Training Regimes for Social Robot NavigationCode0
Cooperation-Aware Reinforcement Learning for Merging in Dense TrafficCode0
Graph Convolutional Reinforcement LearningCode0
Learning Task-relevant Representations for Generalization via Characteristic Functions of Reward Sequence DistributionsCode0
Benchmarking Model-Based Reinforcement LearningCode0
Benchmark Generation Framework with Customizable Distortions for Image Classifier RobustnessCode0
ARES: Alternating Reinforcement Learning and Supervised Fine-Tuning for Enhanced Multi-Modal Chain-of-Thought Reasoning Through Diverse AI FeedbackCode0
Convolutional Reservoir Computing for World ModelsCode0
A framework for reinforcement learning with autocorrelated actionsCode0
Learning to reinforcement learnCode0
Bellman Meets Hawkes: Model-Based Reinforcement Learning via Temporal Point ProcessesCode0
GraphNAS: Graph Neural Architecture Search with Reinforcement LearningCode0
Improving Robustness of Deep Reinforcement Learning Agents: Environment Attack based on the Critic NetworkCode0
Learning Fair Policies in Multiobjective (Deep) Reinforcement Learning with Average and Discounted RewardsCode0
Belief-Enriched Pessimistic Q-Learning against Adversarial State PerturbationsCode0
Behaviour Suite for Reinforcement LearningCode0
Quantum enhancements for deep reinforcement learning in large spacesCode0
Improving the Data-efficiency of Reinforcement Learning by Warm-starting with LLMCode0
Behavior Prior Representation learning for Offline Reinforcement LearningCode0
Convergent Policy Optimization for Safe Reinforcement LearningCode0
A Framework for Automated Cellular Network Tuning with Reinforcement LearningCode0
Iterative Reward Shaping using Human Feedback for Correcting Reward MisspecificationCode0
A nearly Blackwell-optimal policy gradient methodCode0
Arena: a toolkit for Multi-Agent Reinforcement LearningCode0
Control with adaptive Q-learningCode0
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Benchmark Results

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