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 9726–9750 of 15113 papers

TitleStatusHype
Learning from Demonstrations with Energy based Generative Adversarial Imitation Learning—0
Incremental Policy Gradients for Online Reinforcement Learning Control—0
Bounded Myopic Adversaries for Deep Reinforcement Learning Agents—0
Learning Safe Policies with Cost-sensitive Advantage Estimation—0
An Examination of Preference-based Reinforcement Learning for Treatment Recommendation—0
A Robust Fuel Optimization Strategy For Hybrid Electric Vehicles: A Deep Reinforcement Learning Based Continuous Time Design Approach—0
BRAC+: Going Deeper with Behavior Regularized Offline Reinforcement Learning—0
Deep Reinforcement Learning With Adaptive Combined Critics—0
Hindsight Curriculum Generation Based Multi-Goal Experience Replay—0
Distributional Reinforcement Learning for Risk-Sensitive Policies—0
Learning Efficient Planning-based Rewards for Imitation Learning—0
Genetic Soft Updates for Policy Evolution in Deep Reinforcement Learning—0
Entropic Risk-Sensitive Reinforcement Learning: A Meta Regret Framework with Function Approximation—0
Discrete Predictive Representation for Long-horizon Planning—0
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms—0
A REINFORCEMENT LEARNING FRAMEWORK FOR TIME DEPENDENT CAUSAL EFFECTS EVALUATION IN A/B TESTING—0
Learning a Transferable Scheduling Policy for Various Vehicle Routing Problems based on Graph-centric Representation Learning—0
Learning to communicate through imagination with model-based deep multi-agent reinforcement learning—0
Average Reward Reinforcement Learning with Monotonic Policy Improvement—0
Error Controlled Actor-Critic Method to Reinforcement Learning—0
Learning to Dynamically Select Between Reward Shaping Signals—0
Daylight: Assessing Generalization Skills of Deep Reinforcement Learning Agents—0
Learning to Explore with Pleasure—0
Learning Active Learning in the Batch-Mode Setup with Ensembles of Active Learning Agents—0
Learning to Observe with Reinforcement Learning—0
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Benchmark Results

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