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 11501–11525 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning with Smooth Policy—0
Improving the Generalization of Visual Navigation Policies using Invariance Regularization—0
Breaking the Curse of Many Agents: Provable Mean Embedding Q-Iteration for Mean-Field Reinforcement Learning—0
Long-Term Visitation Value for Deep Exploration in Sparse Reward Reinforcement LearningCode0
Double Reinforcement Learning for Efficient and Robust Off-Policy Evaluation—0
Deep Reinforcement Learning with Implicit Human Feedback—0
OPtions as REsponses: Grounding behavioural hierarchies in multi-agent reinforcement learning—0
Reinforcement Learning with Goal-Distance Gradient—0
Optimizing Multiagent Cooperation via Policy Evolution and Shared Experiences—0
“Other-Play” for Zero-Shot Coordination—0
Way Off-Policy Batch Deep Reinforcement Learning of Human Preferences in Dialog—0
Reinforcement Learning with Differential Privacy—0
Responsive Safety in Reinforcement Learning—0
The Natural Lottery Ticket Winner: Reinforcement Learning with Ordinary Neural Circuits—0
SVQN: Sequential Variational Soft Q-Learning Networks—0
Reward-Conditioned PoliciesCode0
Uncertainty-Based Out-of-Distribution Classification in Deep Reinforcement Learning—0
The Gambler's Problem and Beyond—0
Information Theoretic Model Predictive Q-Learning—0
A New Framework for Query Efficient Active Imitation Learning—0
Deep Reinforced Self-Attention Masks for Abstractive Summarization (DR.SAS)—0
World Programs for Model-Based Learning and Planning in Compositional State and Action Spaces—0
Speeding up reinforcement learning by combining attention and agency features—0
Real-time Policy Distillation in Deep Reinforcement Learning—0
Augmented Replay Memory in Reinforcement Learning With Continuous Control—0
Show:102550
← PrevPage 461 of 605Next →

Benchmark Results

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