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 1260112650 of 15113 papers

TitleStatusHype
Stabilizing Unsupervised Environment Design with a Learned Adversary0
Stabilizing Visual Reinforcement Learning via Asymmetric Interactive Cooperation0
Stable and Efficient Policy Evaluation0
Stable Continual Reinforcement Learning via Diffusion-based Trajectory Replay0
Stable deep reinforcement learning method by predicting uncertainty in rewards as a subtask0
Stable Modular Control via Contraction Theory for Reinforcement Learning0
Stable Reinforcement Learning for Optimal Frequency Control: A Distributed Averaging-Based Integral Approach0
Stable Reinforcement Learning with Unbounded State Space0
Stable Relay Learning Optimization Approach for Fast Power System Production Cost Minimization Simulation0
Stackelberg Batch Policy Learning0
Staged Reinforcement Learning for Complex Tasks through Decomposed Environments0
Standardized feature extraction from pairwise conflicts applied to the train rescheduling problem0
StaQ it! Growing neural networks for Policy Mirror Descent0
StarCraft II Build Order Optimization using Deep Reinforcement Learning and Monte-Carlo Tree Search0
StarCraftImage: A Dataset For Prototyping Spatial Reasoning Methods For Multi-Agent Environments0
State2vec: Off-Policy Successor Features Approximators0
State Abstractions for Lifelong Reinforcement Learning0
State-Action Joint Regularized Implicit Policy for Offline Reinforcement Learning0
State Action Separable Reinforcement Learning0
State Advantage Weighting for Offline RL0
State Alignment-based Imitation Learning0
State and Action Factorization in Power Grids0
State-Augmentation Transformations for Risk-Sensitive Reinforcement Learning0
State Augmented Constrained Reinforcement Learning: Overcoming the Limitations of Learning with Rewards0
State-Aware Proximal Pessimistic Algorithms for Offline Reinforcement Learning0
State-based Episodic Memory for Multi-Agent Reinforcement Learning0
State Combinatorial Generalization In Decision Making With Conditional Diffusion Models0
State Dropout-Based Curriculum Reinforcement Learning for Self-Driving at Unsignalized Intersections0
State of the Art of Reinforcement Learning0
State of the Art of User Simulation approaches for conversational information retrieval0
State Regularized Policy Optimization on Data with Dynamics Shift0
State Representation Learning for Goal-Conditioned Reinforcement Learning0
State Representation Learning from Demonstration0
State representation learning with recurrent capsule networks0
State-Separated SARSA: A Practical Sequential Decision-Making Algorithm with Recovering Rewards0
State Space Decomposition and Subgoal Creation for Transfer in Deep Reinforcement Learning0
State-wise Safe Reinforcement Learning: A Survey0
Static Neural Compiler Optimization via Deep Reinforcement Learning0
Statistical CSI-based Beamforming for RIS-Aided Multiuser MISO Systems using Deep Reinforcement Learning0
Statistical Guarantees for Lifelong Reinforcement Learning using PAC-Bayes Theory0
Statistical Inference After Adaptive Sampling for Longitudinal Data0
Statistically Model Checking PCTL Specifications on Markov Decision Processes via Reinforcement Learning0
Statistics and Samples in Distributional Reinforcement Learning0
Learning Skills to Navigate without a Master: A Sequential Multi-Policy Reinforcement Learning Algorithm0
Steady State Analysis of Episodic Reinforcement Learning0
Steady-State Error Compensation for Reinforcement Learning with Quadratic Rewards0
Stealing Deep Reinforcement Learning Models for Fun and Profit0
Stealthy and Efficient Adversarial Attacks against Deep Reinforcement Learning0
Steering LLM Reasoning Through Bias-Only Adaptation0
STEERING: Stein Information Directed Exploration for Model-Based Reinforcement Learning0
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Benchmark Results

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