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 13451–13500 of 15113 papers

TitleStatusHype
Distilled Agent DQN for Provable Adversarial Robustness—0
Exploration by Uncertainty in Reward Space—0
A Convergent Variant of the Boltzmann Softmax Operator in Reinforcement Learning—0
Learning Physics Priors for Deep Reinforcement Learing—0
Learning to Coordinate Multiple Reinforcement Learning Agents for Diverse Query Reformulation—0
Expressiveness in Deep Reinforcement Learning—0
Exploiting Environmental Variation to Improve Policy Robustness in Reinforcement Learning—0
Countering Language Drift via Grounding—0
Deep Reinforcement Learning of Universal Policies with Diverse Environment Summaries—0
Incremental Hierarchical Reinforcement Learning with Multitask LMDPs—0
Convergent Reinforcement Learning with Function Approximation: A Bilevel Optimization Perspective—0
Constraining Action Sequences with Formal Languages for Deep Reinforcement Learning—0
Hybrid Policies Using Inverse Rewards for Reinforcement Learning—0
Dynamic Pricing on E-commerce Platform with Deep Reinforcement Learning—0
A Better Baseline for Second Order Gradient Estimation in Stochastic Computation Graphs—0
DOMAIN ADAPTATION VIA DISTRIBUTION AND REPRESENTATION MATCHING: A CASE STUDY ON TRAINING DATA SELECTION VIA REINFORCEMENT LEARNING—0
Accelerated Value Iteration via Anderson Mixing—0
DEEP ADVERSARIAL FORWARD MODEL—0
Definition and evaluation of model-free coordination of electrical vehicle charging with reinforcement learning—0
Guided Exploration in Deep Reinforcement Learning—0
Controllable Neural Story Plot Generation via Reward ShapingCode0
Learning Navigation Behaviors End-to-End with AutoRL—0
Learning through Probing: a decentralized reinforcement learning architecture for social dilemmas—0
AlphaSeq: Sequence Discovery with Deep Reinforcement Learning—0
Omega-Regular Objectives in Model-Free Reinforcement Learning—0
S-RL Toolbox: Environments, Datasets and Evaluation Metrics for State Representation LearningCode0
Resilient Computing with Reinforcement Learning on a Dynamical System: Case Study in Sorting—0
Anderson Acceleration for Reinforcement Learning—0
Hierarchical Deep Multiagent Reinforcement Learning with Temporal Abstraction—0
Floyd-Warshall Reinforcement Learning: Learning from Past Experiences to Reach New Goals—0
Low Precision Policy Distillation with Application to Low-Power, Real-time Sensation-Cognition-Action Loop with Neuromorphic Computing—0
EpiRL: A Reinforcement Learning Agent to Facilitate Epistasis Detection—0
Better Safe than Sorry: Evidence Accumulation Allows for Safe Reinforcement LearningCode0
Personalized Education at Scale—0
SDN Flow Entry Management Using Reinforcement Learning—0
On Reinforcement Learning for Full-length Game of StarCraft—0
A Learning Framework for High Precision Industrial Assembly—0
Geometric Multi-Model Fitting by Deep Reinforcement Learning—0
Finite Sample Analysis of the GTD Policy Evaluation Algorithms in Markov Setting—0
Interpretable Multi-Objective Reinforcement Learning through Policy Orchestration—0
Constrained Exploration and Recovery from Experience ShapingCode0
Target Transfer Q-Learning and Its Convergence Analysis—0
Sim-to-Real Transfer of Robot Learning with Variable Length Inputs—0
Benchmarking Reinforcement Learning Algorithms on Real-World RobotsCode0
IntelligentCrowd: Mobile Crowdsensing via Multi-Agent Reinforcement Learning—0
Dynamic Weights in Multi-Objective Deep Reinforcement LearningCode0
Interpretable Reinforcement Learning with Ensemble Methods—0
Prosocial or Selfish? Agents with different behaviors for Contract Negotiation using Reinforcement Learning—0
Model-Free Adaptive Optimal Control of Episodic Fixed-Horizon Manufacturing Processes using Reinforcement LearningCode0
SCC-rFMQ Learning in Cooperative Markov Games with Continuous Actions—0
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Benchmark Results

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