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

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Showing 11026–11050 of 15113 papers

TitleStatusHype
Is Long Horizon Reinforcement Learning More Difficult Than Short Horizon Reinforcement Learning?—0
Improving Robustness via Risk Averse Distributional Reinforcement Learning—0
Exploration in Reinforcement Learning with Deep Covering Options—0
Episodic Reinforcement Learning with Associative Memory—0
Learning Efficient Parameter Server Synchronization Policies for Distributed SGD—0
Synthesizing Programmatic Policies that Inductively Generalize—0
Model Based Reinforcement Learning for Atari—0
Model-based reinforcement learning for biological sequence design—0
Toward Evaluating Robustness of Deep Reinforcement Learning with Continuous Control—0
Posterior sampling for multi-agent reinforcement learning: solving extensive games with imperfect information—0
The Ingredients of Real World Robotic Reinforcement Learning—0
Reinforcement learning of minimalist grammars—0
Unsupervised Learning of KB Queries in Task-Oriented Dialogs—0
Towards Embodied Scene Description—0
Out-of-the-box channel pruned networks—0
Plan-Space State Embeddings for Improved Reinforcement Learning—0
DSAC: Distributional Soft Actor Critic for Risk-Sensitive Reinforcement Learning—0
GCN-RL Circuit Designer: Transferable Transistor Sizing with Graph Neural Networks and Reinforcement Learning—0
Delay-aware Resource Allocation in Fog-assisted IoT Networks Through Reinforcement Learning—0
Improving Factual Consistency Between a Response and Persona Facts—0
Breaking (Global) Barriers in Parallel Stochastic Optimization with Wait-Avoiding Group Averaging—0
Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning—0
Graph-based State Representation for Deep Reinforcement LearningCode0
Reduced-Dimensional Reinforcement Learning Control using Singular Perturbation Approximations—0
Whittle index based Q-learning for restless bandits with average reward—0
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Benchmark Results

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