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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5601–5625 of 15113 papers

TitleStatusHype
Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving—0
Safe Multi-Agent Reinforcement Learning via Shielding—0
Safe Planning and Policy Optimization via World Model Learning—0
Safe Policies for Reinforcement Learning via Primal-Dual Methods—0
Safe Policy Improvement for POMDPs via Finite-State Controllers—0
Safe Policy Improvement in Constrained Markov Decision Processes—0
Safe Policy Search for Lifelong Reinforcement Learning with Sublinear Regret—0
Safe RAN control: A Symbolic Reinforcement Learning Approach—0
SAFER: Data-Efficient and Safe Reinforcement Learning Through Skill Acquisition—0
SAFER: Data-Efficient and Safe Reinforcement Learning via Skill Acquisition—0
Safer Deep RL with Shallow MCTS: A Case Study in Pommerman—0
SAFER: Safe Collision Avoidance using Focused and Efficient Trajectory Search with Reinforcement Learning—0
Safe Reinforcement Learning-based Control for Hydrogen Diesel Dual-Fuel Engines—0
Safe Reinforcement Learning-Based Eco-Driving Control for Mixed Traffic Flows With Disturbances—0
Safe reinforcement learning control for continuous-time nonlinear systems without a backup controller—0
Safe Reinforcement Learning for an Energy-Efficient Driver Assistance System—0
A Safe Reinforcement Learning Architecture for Antenna Tilt Optimisation—0
Safe Reinforcement Learning for a Robot Being Pursued but with Objectives Covering More Than Capture-avoidance—0
Safe Reinforcement Learning for Autonomous Vehicles through Parallel Constrained Policy Optimization—0
Safe Reinforcement Learning for Grid Voltage Control—0
Safe Reinforcement Learning for Legged Locomotion—0
Safe reinforcement learning for multi-energy management systems with known constraint functions—0
Safe Reinforcement Learning for Power System Control: A Review—0
Safe Reinforcement Learning for Real-World Engine Control—0
Safe Reinforcement Learning in a Simulated Robotic Arm—0
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Benchmark Results

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