Reinforcement Learning
On Reward-Free Reinforcement Learning with Linear Function Approximation
During the exploration phase, an agent collects samples without using a pre-specified reward function.
On Reward-Free Reinforcement Learning with Linear Function Approximation
During the exploration phase, an agent collects samples without using a pre-specified reward function.
SPD: Synergy Pattern Diversifying Oriented Unsupervised Multi-agent Reinforcement Learning
As for the single agent, unsupervised learning has been incorporated into RL to acquire diverse skills for the agent without extrinsic reward from the environment, and this scenario is known as unsupervised reinforcement learning (URL).
A Finite-Time Analysis of Two Time-Scale Actor-Critic Methods
In this work, we provide a non-asymptotic analysis for two timescale actor-critic methods under non-i.i.d.
80b7bec60081f95d900973509744a306-Paper-Conference.pdf
As efficient exploration in BAMDPs hinges upon the judicious acquisition of information, our complexity measure highlights the worst-case difficulty of gathering information and exhausting epistemic uncertainty.
