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 9026–9050 of 15113 papers

TitleStatusHype
Daylight: Assessing Generalization Skills of Deep Reinforcement Learning Agents—0
Incremental Policy Gradients for Online Reinforcement Learning Control—0
An Examination of Preference-based Reinforcement Learning for Treatment Recommendation—0
CAT-SAC: Soft Actor-Critic with Curiosity-Aware Entropy Temperature—0
Explainable Reinforcement Learning Through Goal-Based Explanations—0
Cross-State Self-Constraint for Feature Generalization in Deep Reinforcement Learning—0
Alpha-DAG: a reinforcement learning based algorithm to learn Directed Acyclic Graphs—0
Constrained Reinforcement Learning With Learned Constraints—0
Error Controlled Actor-Critic Method to Reinforcement Learning—0
Learning to Explore with Pleasure—0
Learning Efficient Planning-based Rewards for Imitation Learning—0
Average Reward Reinforcement Learning with Monotonic Policy Improvement—0
Learning from Demonstrations with Energy based Generative Adversarial Imitation Learning—0
Deep Reinforcement Learning With Adaptive Combined Critics—0
Entropic Risk-Sensitive Reinforcement Learning: A Meta Regret Framework with Function Approximation—0
Discrete Predictive Representation for Long-horizon Planning—0
BRAC+: Going Deeper with Behavior Regularized Offline Reinforcement Learning—0
Compute- and Memory-Efficient Reinforcement Learning with Latent Experience Replay—0
Learning Predictive Communication by Imagination in Networked System Control—0
Learning Safe Policies with Cost-sensitive Advantage Estimation—0
Adaptive Multi-model Fusion Learning for Sparse-Reward Reinforcement Learning—0
Understanding and Leveraging Causal Relations in Deep Reinforcement Learning—0
Self-Supervised Continuous Control without Policy Gradient—0
Unbiased learning with State-Conditioned Rewards in Adversarial Imitation Learning—0
TEAC: Intergrating Trust Region and Max Entropy Actor Critic for Continuous ControlCode0
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Benchmark Results

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