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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 6551–6600 of 15113 papers

TitleStatusHype
Distributional Decision Transformer for Hindsight Information Matching—0
Distributional Hamilton-Jacobi-Bellman Equations for Continuous-Time Reinforcement Learning—0
Distributionally Adaptive Meta Reinforcement Learning—0
Distributionally-Constrained Policy Optimization via Unbalanced Optimal Transport—0
Distributionally Robust Imitation Learning—0
Distributionally Robust Model-Based Offline Reinforcement Learning with Near-Optimal Sample Complexity—0
Distributionally Robust Offline Reinforcement Learning with Linear Function Approximation—0
Distributionally Robust Reinforcement Learning—0
Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithm—0
Distributional Method for Risk Averse Reinforcement Learning—0
Distributional Perturbation for Efficient Exploration in Distributional Reinforcement Learning—0
Distributional Reinforcement Learning for Efficient Exploration—0
Distributional Reinforcement Learning for Risk-Sensitive Policies—0
Distributional Reinforcement Learning for mmWave Communications with Intelligent Reflectors on a UAV—0
Distributional Reinforcement Learning for Scheduling of Chemical Production Processes—0
Distributional reinforcement learning with linear function approximation—0
Distributional Reinforcement Learning with Ensembles—0
Distributional Reinforcement Learning with Monotonic Splines—0
Distributional Reinforcement Learning with Dual Expectile-Quantile Regression—0
Distributional Reinforcement Learning with Online Risk-awareness Adaption—0
Distributional Reward Decomposition for Reinforcement Learning—0
Distributional Robustness and Regularization in Reinforcement Learning—0
DSAC: Distributional Soft Actor Critic for Risk-Sensitive Reinforcement Learning—0
Distributional Soft Actor-Critic with Harmonic Gradient for Safe and Efficient Autonomous Driving in Multi-lane Scenarios—0
Distributive Dynamic Spectrum Access through Deep Reinforcement Learning: A Reservoir Computing Based Approach—0
District Cooling System Control for Providing Operating Reserve based on Safe Deep Reinforcement Learning—0
Disturbing Reinforcement Learning Agents with Corrupted Rewards—0
DITTO: Offline Imitation Learning with World Models—0
Divergence-Regularized Multi-Agent Actor-Critic—0
Divergent representations of ethological visual inputs emerge from supervised, unsupervised, and reinforcement learning—0
Diverse Exploration for Fast and Safe Policy Improvement—0
Diverse Priors for Deep Reinforcement Learning—0
Diverse Projection Ensembles for Distributional Reinforcement Learning—0
Diverse Randomized Value Functions: A Provably Pessimistic Approach for Offline Reinforcement Learning—0
Diverse Transformer Decoding for Offline Reinforcement Learning Using Financial Algorithmic Approaches—0
Diversify & Conquer: Outcome-directed Curriculum RL via Out-of-Distribution Disagreement—0
Diversity-Aware Policy Optimization for Large Language Model Reasoning—0
Diversity-Driven Exploration Strategy for Deep Reinforcement Learning—0
Diversity Progress for Goal Selection in Discriminability-Motivated RL—0
Diversity-Promoting Deep Reinforcement Learning for Interactive Recommendation—0
Diversity Through Exclusion (DTE): Niche Identification for Reinforcement Learning through Value-Decomposition—0
Divide-and-Conquer Monte Carlo Tree Search For Goal-Directed Planning—0
Divide-and-Conquer Monte Carlo Tree Search—0
Divide-Fuse-Conquer: Eliciting "Aha Moments" in Multi-Scenario Games—0
DIVINE: A Generative Adversarial Imitation Learning Framework for Knowledge Graph Reasoning—0
DJ-MC: A Reinforcement-Learning Agent for Music Playlist Recommendation—0
DL-DRL: A double-level deep reinforcement learning approach for large-scale task scheduling of multi-UAV—0
dm_control: Software and Tasks for Continuous Control—0
DNN-Opt: An RL Inspired Optimization for Analog Circuit Sizing using Deep Neural Networks—0
Do Androids Dream of Electric Fences? Safety-Aware Reinforcement Learning with Latent Shielding—0
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Benchmark Results

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