Vanishing Bias Heuristic-guided Reinforcement Learning Algorithm

06/17/2023
by   Qinru Li, et al.
0

Reinforcement Learning has achieved tremendous success in the many Atari games. In this paper we explored with the lunar lander environment and implemented classical methods including Q-Learning, SARSA, MC as well as tiling coding. We also implemented Neural Network based methods including DQN, Double DQN, Clipped DQN. On top of these, we proposed a new algorithm called Heuristic RL which utilizes heuristic to guide the early stage training while alleviating the introduced human bias. Our experiments showed promising results for our proposed methods in the lunar lander environment.

READ FULL TEXT

page 9

page 10

page 11

page 12

page 13

research
11/05/2017

Double Q(σ) and Q(σ, λ): Unifying Reinforcement Learning Control Algorithms

Temporal-difference (TD) learning is an important field in reinforcement...
research
06/05/2021

Heuristic-Guided Reinforcement Learning

We provide a framework for accelerating reinforcement learning (RL) algo...
research
02/23/2018

Weighted Double Deep Multiagent Reinforcement Learning in Stochastic Cooperative Environments

Despite single agent deep reinforcement learning has achieved significan...
research
10/07/2022

Elastic Step DQN: A novel multi-step algorithm to alleviate overestimation in Deep QNetworks

Deep Q-Networks algorithm (DQN) was the first reinforcement learning alg...
research
11/19/2018

Reinforcement Learning with A* and a Deep Heuristic

A* is a popular path-finding algorithm, but it can only be applied to th...
research
02/28/2021

Ensemble Bootstrapping for Q-Learning

Q-learning (QL), a common reinforcement learning algorithm, suffers from...
research
08/18/2011

Feature Reinforcement Learning In Practice

Following a recent surge in using history-based methods for resolving pe...

Please sign up or login with your details

Forgot password? Click here to reset