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 2941–2950 of 15113 papers

TitleStatusHype
A Hybrid Neuro-Symbolic approach for Text-Based Games using Inductive Logic Programming—0
Adaptive Aggregation for Safety-Critical Control—0
Deceptive Reinforcement Learning for Privacy-Preserving Planning—0
INTAGS: Interactive Agent-Guided Simulation—0
At Human Speed: Deep Reinforcement Learning with Action Delay—0
Adaptive Adversarial Training for Meta Reinforcement Learning—0
A Hybrid Approach for Reinforcement Learning Using Virtual Policy Gradient for Balancing an Inverted Pendulum—0
A Fast Convergence Theory for Offline Decision Making—0
ACE: An Actor Ensemble Algorithm for Continuous Control with Tree Search—0
AACC: Asymmetric Actor-Critic in Contextual Reinforcement Learning—0
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Benchmark Results

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