Continuous Deep Q-Learning with Model-based Acceleration: Appendix
نویسندگان
چکیده
The iLQG algorithm optimizes trajectories by iteratively constructing locally optimal linear feedback controllers under a local linearization of the dynamics p(xt+1|xt,ut) = N (fxtxt + futut,Ft) and a quadratic expansion of the rewards r(xt,ut) (Tassa et al., 2012). Under linear dynamics and quadratic rewards, the action-value function Q(xt,ut) and value function V (xt) are locally quadratic and can be computed by dynamics programming.1
منابع مشابه
Operation Scheduling of MGs Based on Deep Reinforcement Learning Algorithm
: In this paper, the operation scheduling of Microgrids (MGs), including Distributed Energy Resources (DERs) and Energy Storage Systems (ESSs), is proposed using a Deep Reinforcement Learning (DRL) based approach. Due to the dynamic characteristic of the problem, it firstly is formulated as a Markov Decision Process (MDP). Next, Deep Deterministic Policy Gradient (DDPG) algorithm is presented t...
متن کاملContinuous Deep Q-Learning with Model-based Acceleration
Model-free reinforcement learning has been successfully applied to a range of challenging problems, and has recently been extended to handle large neural network policies and value functions. However, the sample complexity of modelfree algorithms, particularly when using highdimensional function approximators, tends to limit their applicability to physical systems. In this paper, we explore alg...
متن کاملA Q-learning Based Continuous Tuning of Fuzzy Wall Tracking
A simple easy to implement algorithm is proposed to address wall tracking task of an autonomous robot. The robot should navigate in unknown environments, find the nearest wall, and track it solely based on locally sensed data. The proposed method benefits from coupling fuzzy logic and Q-learning to meet requirements of autonomous navigations. Fuzzy if-then rules provide a reliable decision maki...
متن کاملTowards Monocular Vision based Obstacle Avoidance through Deep Reinforcement Learning
Obstacle avoidance is a fundamental requirement for autonomous robots which operate in, and interact with, the real world. When perception is limited to monocular vision avoiding collision becomes significantly more challenging due to the lack of 3D information. Conventional path planners for obstacle avoidance require tuning a number of parameters and do not have the ability to directly benefi...
متن کاملBayesian Deep Q-Learning via Continuous-Time Flows
Efficient exploration in reinforcement learning (RL) can be achieved by incorporating uncertainty into model predictions. Bayesian deep Q-learning provides a principle way for this by modeling Q-values as probability distributions. We propose an efficient algorithm for Bayesian deep Q-learning by posterior sampling actions in the Q-function via continuous-time flows (CTFs), achieving efficient ...
متن کامل