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 10051–10075 of 15113 papers

TitleStatusHype
Distributional Reinforcement Learning with Ensembles—0
Distributional Reinforcement Learning with Monotonic Splines—0
Distributional Reinforcement Learning with Dual Expectile-Quantile Regression—0
Distributional Reinforcement Learning with Online Risk-awareness Adaption—0
Distributional Reward Decomposition for Reinforcement Learning—0
Distributional Robustness and Regularization in Reinforcement Learning—0
DSAC: Distributional Soft Actor Critic for Risk-Sensitive Reinforcement Learning—0
Distributional Soft Actor-Critic with Harmonic Gradient for Safe and Efficient Autonomous Driving in Multi-lane Scenarios—0
Distributive Dynamic Spectrum Access through Deep Reinforcement Learning: A Reservoir Computing Based Approach—0
District Cooling System Control for Providing Operating Reserve based on Safe Deep Reinforcement Learning—0
Disturbing Reinforcement Learning Agents with Corrupted Rewards—0
DITTO: Offline Imitation Learning with World Models—0
Divergence-Regularized Multi-Agent Actor-Critic—0
Divergent representations of ethological visual inputs emerge from supervised, unsupervised, and reinforcement learning—0
Diverse Exploration for Fast and Safe Policy Improvement—0
Diverse Priors for Deep Reinforcement Learning—0
Diverse Projection Ensembles for Distributional Reinforcement Learning—0
Diverse Randomized Value Functions: A Provably Pessimistic Approach for Offline Reinforcement Learning—0
Diverse Transformer Decoding for Offline Reinforcement Learning Using Financial Algorithmic Approaches—0
Diversify & Conquer: Outcome-directed Curriculum RL via Out-of-Distribution Disagreement—0
Diversity-Aware Policy Optimization for Large Language Model Reasoning—0
Diversity-Driven Exploration Strategy for Deep Reinforcement Learning—0
Diversity Progress for Goal Selection in Discriminability-Motivated RL—0
Diversity-Promoting Deep Reinforcement Learning for Interactive Recommendation—0
Diversity Through Exclusion (DTE): Niche Identification for Reinforcement Learning through Value-Decomposition—0
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Benchmark Results

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