Reward-free RL via Sample-Efficient Representation Learning 讲座摘要:As reward-free reinforcement learning (RL) becomes a powerful framework for a variety of multi-objective applications, representation learning arises as an effective technique to deal with the curse of dimensionality in reward-free RL...
Exploration is widely regarded as one of the most challenging aspects of reinforcement learning (RL), with many naive approaches succumbing to exponential sample complexity. To isolate the challenges of exploration, we propose a new “reward-free RL” framework. In the exploration phase, the ag...
To isolate the challenges of exploration, we propose a new "reward-free RL" framework. In the exploration phase, the agent first collects trajectories from an MDP M without a pre-specified reward function. After exploration, it is tasked with computing near-optimal policies under for M for a...
Exploration is widely regarded as one of the most challenging aspects of reinforcement learning (RL), with many naive approaches succumbing to exponential sample complexity. To isolate the challenges of exploration, we propose a new “reward-free RL” framework. In the exploration phase, the agent...
文章提出的这个范式,在第一个探索阶段只做 reward-free 的探索,这个交互和标准的 RL 交互的区别就在于环境不返回奖励。相比于标准 RL,其他方面都一样,比如都具有一个固定的初始状态分布,并且要从该分布出发根据 transition dynamics 来访问各个状态。 3、Overview ...
文章提出了要对policy space进行压缩的问题,旨在找到一种压缩方法,获得一个compressed policy space,使得常规的RL任务prediction/control 能够高效且不失最优性地进行 显而易见,本文的动机具有合理性和重要意义,但是同时并不是一个简单的问题 Preliminaries
showthe feasibility of an attacker to act strategically without knowledge of the victim’smotives even if the victim’s reward information is protected.1 IntroductionThe recent accomplishments of RL and self-play in Go [ 22 ], Starcraft 2 [ 23 ], DOTA 2 [ 3 ], and poker[ 4 ] are seen...
Optimal Horizon-Free Reward-Free Exploration for Linear Mixture MDPs 线性混合MDP的最优无水平无报酬探索 We study reward-free reinforcement learning (RL) with linear function approximation, where the agent works in two phases 研究了线性函数无报酬强化学习(RL 近似值,其中代理分两个阶段工作...
内容提示: Uncertainty-Aware Reward-Free Exploration with General FunctionApproximationJunkai Zhang * 1 Weitong Zhang * 1 Dongruo Zhou 2 Quanquan Gu 1AbstractMastering multiple tasks through exploration andlearning in an environment poses a signif icantchallenge in reinforcement learning (RL). Un-...
During RL, we need to evaluate the agent A many times. If we want to use a learned reward function we may need to evaluate A more times. And if we want to train a policy which remains benign off of the training distribution, we may need to evaluate A more times (e.g. since we ...