RBED: Reward Based Epsilon Decay
$\varepsilon$-greedy is a policy used to balance exploration and exploitation in many reinforcement learning setting. In cases where the agent uses some on-policy algorithm to learn optimal behaviour, it makes sense for the agent to explore more initially and eventually exploit more as it approaches the target behaviour. This shift from heavy exploration to heavy exploitation can be represented as decay in the $\varepsilon$ value, where $\varepsilon$ depicts the how much an agent is allowed to explore. This paper proposes a new approach to this $\varepsilon$ decay where the decay is based on feedback from the environment. This paper also compares and contrasts one such approach based on rewards and compares it against standard exponential decay. The new approach, in the environments tested, produces more consistent results that on average perform better.
Code (0)
등록된 구현이 없습니다.
Tasks
Reinforcement LearningReinforcement Learning (RL)Similar Papers 제목 키워드 기반
Perturbational Complexity by Distribution Mismatch: A Systematic Analysis of Reinforcement Learning in Reproducing Kernel Hilbert Space
Most existing theoretical analysis of reinforcement learning (RL) is limited to the tabular setting or linear models due to the difficulty in dealing with function approximation in high dimensional space with an uncertai…
Reinforcement Learning (RL)Escaping Saddle Points for Nonsmooth Weakly Convex Functions via Perturbed Proximal Algorithms
We propose perturbed proximal algorithms that can provably escape strict saddles for nonsmooth weakly convex functions. The main results are based on a novel characterization of $\epsilon$-approximate local minimum for n…
Kernel $ε$-Greedy for Multi-Armed Bandits with Covariates
We consider the $\epsilon$-greedy strategy for the multi-arm bandit with covariates (MABC) problem, where the mean reward functions are assumed to lie in a reproducing kernel Hilbert space (RKHS). We propose to estimate …
Multi-Armed BanditsFaster Perturbed Stochastic Gradient Methods for Finding Local Minima
Escaping from saddle points and finding local minimum is a central problem in nonconvex optimization. Perturbed gradient methods are perhaps the simplest approach for this problem. However, to find $(\epsilon, \sqrt{\eps…
DQN Performance with Epsilon Greedy Policies and Prioritized Experience Replay
We present a detailed study of Deep Q-Networks in finite environments, emphasizing the impact of epsilon-greedy exploration schedules and prioritized experience replay. Through systematic experimentation, we evaluate how…
Reinforcement Learning