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 14001–14050 of 15113 papers

TitleStatusHype
Cooperative Multi-Agent Reinforcement Learning for Low-Level Wireless Communication—0
Deep Reinforcement Fuzzing—0
Deep Reinforcement Learning of Cell Movement in the Early Stage of C. elegans Embryogenesis—0
Autonomous Driving in Reality with Reinforcement Learning and Image Translation—0
Expected Policy Gradients for Reinforcement Learning—0
DeepTraffic: Crowdsourced Hyperparameter Tuning of Deep Reinforcement Learning Systems for Multi-Agent Dense Traffic NavigationCode0
Distributed Deep Reinforcement Learning: Learn how to play Atari games in 21 minutesCode0
Trading the Twitter Sentiment with Reinforcement Learning—0
Sample-Efficient Reinforcement Learning through Transfer and Architectural Priors—0
Competitive Multi-agent Inverse Reinforcement Learning with Sub-optimal DemonstrationsCode0
Using reinforcement learning to learn how to play text-based gamesCode0
Faster Deep Q-learning using Neural Episodic Control—0
Deep Reinforcement Learning based Optimal Control of Hot Water Systems—0
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic ActorCode1
Jointly Learning to Construct and Control Agents using Deep Reinforcement LearningCode0
DeepMind Control SuiteCode1
Learning objects from pixels—0
AUTOMATA GUIDED HIERARCHICAL REINFORCEMENT LEARNING FOR ZERO-SHOT SKILL COMPOSITION—0
Latent forward model for Real-time Strategy game planning with incomplete information—0
Faster Reinforcement Learning with Expert State Sequences—0
Learning to Treat Sepsis with Multi-Output Gaussian Process Deep Recurrent Q-Networks—0
Learning Dynamic State Abstractions for Model-Based Reinforcement Learning—0
Learning Gaussian Policies from Smoothed Action Value Functions—0
Domain Adaptation for Deep Reinforcement Learning in Visually Distinct Games—0
Exploring Deep Recurrent Models with Reinforcement Learning for Molecule Design—0
Combination of Supervised and Reinforcement Learning For Vision-Based Autonomous Control—0
Alpha-divergence bridges maximum likelihood and reinforcement learning in neural sequence generation—0
A dynamic game approach to training robust deep policies—0
Autonomous Vehicle Fleet Coordination With Deep Reinforcement Learning—0
Reward Estimation via State Prediction—0
Neuron as an Agent—0
Using Deep Reinforcement Learning to Generate Rationales for Molecules—0
Long Term Memory Network for Combinatorial Optimization Problems—0
Policy Gradient For Multidimensional Action Spaces: Action Sampling and Entropy Bonus—0
LSD-Net: Look, Step and Detect for Joint Navigation and Multi-View Recognition with Deep Reinforcement Learning—0
Predicting Multiple Actions for Stochastic Continuous Control—0
Neural Task Graph Execution—0
Representing Entropy : A short proof of the equivalence between soft Q-learning and policy gradients—0
Model-based imitation learning from state trajectories—0
Now I Remember! Episodic Memory For Reinforcement Learning—0
Reinforcement Learning via Replica Stacking of Quantum Measurements for the Training of Quantum Boltzmann Machines—0
Universal Agent for Disentangling Environments and Tasks—0
Residual Loss Prediction: Reinforcement Learning With No Incremental FeedbackCode0
NerveNet: Learning Structured Policy with Graph Neural NetworksCode0
LatentPoison -- Adversarial Attacks On The Latent Space—0
Do Deep Reinforcement Learning Algorithms really Learn to Navigate?—0
Learning Robust Rewards with Adverserial Inverse Reinforcement Learning—0
Learning an Embedding Space for Transferable Robot Skills—0
A Hierarchical Model for Device Placement—0
Action-dependent Control Variates for Policy Optimization via Stein Identity—0
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Benchmark Results

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