Jun 24, 2019 · Abstract:In classical Q-learning, the objective is to maximize the sum of discounted rewards through iteratively using the Bellman equation ...
Abstract. In classical Q-learning, the objective is to maximize the sum of discounted rewards through iteratively using the Bellman equation as an update, ...
Hadi S. Jomaa, Josif Grabocka, Lars Schmidt-Thieme: In Hindsight: A Smooth Reward for Steady Exploration. CoRR abs/1906.09781 (2019). manage site settings.
During each step, the reinforcement learning agent was given a reward of −0.1 to encourage exploration and quick movement to the target. When the endpoint ...
At each step, the agent receives a reward, which ideally reflects how well it is achieving its goal. Traditional Rl methods leverage these rewards to learn good ...
Missing: Smooth Steady
One of the key reasons for the high sample complexity in reinforcement learning. (RL) is the inability to transfer knowledge from one task to another.
Jul 8, 2024 · Reward shaping Learning with sparse rewards can be challenging since the agent has to explore the environment extensively to discover the right ...
Oct 10, 2019 · This paper proposes an advanced policy optimization method with hindsight experience for sparse reward reinforcement learning.
Sep 26, 2022 · We emphasize that manipulation hindsight rewards to counter bias are fundamentally different from reward shaping. In general, bias ...
In Hindsight: A Smooth Reward for Steady Exploration · no code implementations • 24 Jun 2019 • Hadi S. Jomaa, Josif Grabocka, Lars Schmidt-Thieme. In classical ...