A DQN, or Deep Q-Network, approximates a state-value function in a Q-Learning framework with a neural network. In the Atari Games case, they take in several frames of the game as an input and output state values for each action as an output. It is usually used in conjunction with Experience Replay, for storing the episode steps in memory for off-policy learning, where samples are drawn from ...In this paper, the trained model was tested after 2000 training stages. The step length of test experiment in this paper is set to 10 stages and each stage is set to 10000 time-steps. During the testing phase, the behavior strategy of the agent is ε-greedy and ε is set to 0.05. In order to reflect the generalization of the model and reduce ...DQN is quite an important algorithm in Deep RL. It lays the foundation for the field, with the principles introduced in the paper being used even today. Its success in using Deep Neural Networks to perform well across a range of environments causes it to be often dubbed as the "ImageNet of Deep RL".

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Algorithm 3 shows the pseudo-code for the DQN algorithm. Sustainability 2021, 13, 11254 7 of 18 Algorithm 3 Deep Q-learning with replay memory. This paper proposes a new multi-stage TNEP method based on the deep Q-network (DQN) algorithm, which can solve the multi-stage TNEP problem based on a static TNEP model. The main purpose of this research is to provide grid planners with a simple and effective multi-stage TNEP method, whichThis paper proposes a new multi-stage TNEP method based on the deep Q-network (DQN) algorithm, which can solve the multi-stage TNEP problem based on a static TNEP model. The main purpose of this research is to provide grid planners with a simple and effective multi-stage TNEP method, whichSolution 2: experience replay Deep Q-Networks (DQN): Experience Replay To remove correlations, build data-set from agent's own experience s1, a1, r2, s2 s2, a2, r3, s3! s, a, r , s0 s3, a3, r4, s4 st, at, rt+1, st+1! st, at, rt+1, st+1 Sample experiences from data-set and apply update

Oct 14, 2021 · This paper reports on a comparison of gradient-based Deep Q-Network (DQN) and Double DQN algorithms, with gradient-free (population-based) Genetic Algorithms (GA), on learning to play the Flappy Bird game that involves complex sensory inputs. Despite the empirical success of the deep Q network (DQN) reinforcement learning algorithm and its variants, DQN is still not well understood and it does not guarantee convergence. In this work, we show that DQN can diverge and cease to operate in realistic settings. Although there exist gradient-based convergent methods, we show that they actually have inherent problems in learning behaviour ...In this paper, a Deep Q-Network (DQN) based multi-agent multi-user power allocation algorithm is proposed for hybrid networks composed of radio frequency (RF) and visible light communication (VLC) access points (APs). The users are capable of multihoming, which can bridge RF and VLC links for accommodating their bandwidth requirements. By leveraging a non-cooperative multi-agent DQN algorithm ...In our paper "Evolving ... To further control the cost of training, we seeded the initial population with human-designed RL algorithms such as DQN. Overview of meta-learning method. Newly proposed algorithms must first perform well on a hurdle environment before being trained on a set of harder environments. Algorithm performance is used to ...

An Improved Algorithm of Robot Path Planning in Complex Environment Based on Double DQN. 07/23/2021 ∙ by Fei Zhang, et al. ∙ Shanghai Jiao Tong University ∙ 0 ∙ share . Deep Q Network (DQN) has several limitations when applied in planning a path in environment with a number of dilemmas according to our experiment.After implementing these, we will have a fully fledged DQN, as specified by the original paper 1. Target Network. During training of our algorithm we set targets for gradient descend as: \[Q(s, a) \xrightarrow{} r + \gamma \max_a Q(s', a)\] We see that the target depends on the current network. A neural network works as a whole, and so each ...An Improved Algorithm of Robot Path Planning in Complex Environment Based on Double DQN. 07/23/2021 ∙ by Fei Zhang, et al. ∙ Shanghai Jiao Tong University ∙ 0 ∙ share . Deep Q Network (DQN) has several limitations when applied in planning a path in environment with a number of dilemmas according to our experiment.

In addition, the authors also showed that Thompson sampling can work with bootstrapped DQN reinforcement learning algorithm. For validation, the authors tested the proposed algorithm on various Atari benchmark gaming suites. This paper tries to use a Thompson sampling like approach to make decisions. Thompson SamplingIn this paper, a Deep Q-Network (DQN) based multi-agent multi-user power allocation algorithm is proposed for hybrid networks composed of radio frequency (RF) and visible light communication (VLC) access points (APs). The users are capable of multihoming, which can bridge RF and VLC links for accommodating their bandwidth requirements. By leveraging a non-cooperative multi-agent DQN algorithm ...In this paper , a game system based on turn-based confrontation is designed and implemented with the state-of-the-art deep reinforcement learning models. Specifically, we first design a Q-learning algorithm to achieve intelligent decision-making, which is based the DQN(Deep Q Network) to model the complex game behaviors.It's an improvement over the DQN code presented in last chapter and should be easy to understand. The DQN architecture from the original paper 4 is implemented, although with some differences. In short, the algorithm first rescales the screen to 84x84 pixels and extracts luminance. Then it feeds last two screens as an input to the neural network.

The popular Q-learning algorithm is known to overestimate action values under certain conditions. It was not previously known whether, in practice, such overestimations are common, whether they harm performance, and whether they can generally be prevented. In this paper, we answer all these questions affirmatively. In particular, we first show that the recent DQN algorithm, which combines Q ...This paper adds recurrency to a DQN by replacing the first post-convolutional fully-connected layer with a recurrent LSTM. Single image input cannot reveal time related information (e.g. velocity, direction, etc). Therefore, DQN algorithm stacks 4 time series images to get this kind of information.We recently published a paper on deep reinforcement learning with Double Q-learning, demonstrating that Q-learning learns overoptimistic action values when combined with deep neural networks, even on deterministic environments such as Atari video games, and that this can be remedied by using a variant of Double Q-learning. The resulting Double DQN algorithm greatly improves over the ...

Algorithm 3 shows the pseudo-code for the DQN algorithm. Sustainability 2021, 13, 11254 7 of 18 Algorithm 3 Deep Q-learning with replay memory. The average values of cumulative rewards in each iteration of the DQN, DuDQN, and ECM-DQN are shown in Fig.4 where the effect of SDRL-ECM is the best among three methods. The DQN can perceive only four continuous image frames nearest to the given state at each time, the value function estimated by the DuDQN is more precise than that by the DQN. Oct 12, 2021 · In this paper, we propose a reinforcement learning (RL)-based framework for UAV-BS beam alignment using deep Q-Network (DQN) in a mmWave setting. We consider uplink communications where the UAV hovers around 5G new radio (NR) BS coverage area, with varying channel conditions. The proposed learning framework uses the location information to ...

Double DQN [1] One of the problems of the DQN algorithm is that it overestimates the true rewards; the Q-values think the agent is going to obtain a higher return than what it will obtain in reality.To fix this, the authors of the Double DQN algorithm [1] suggest using a simple trick: decoupling the action selection from the action evaluation.Instead of using the same Bellman equation as in ...The D-DQN-LSTM algorithm proposed in this paper is superior to GA and deep learning in terms of the running time, total path, collision avoidance performance against moving obstacles, and planning time for each step. The forces and torques required by the planning algorithms are also feasible for the actuator. According to the above analysis ...This paper demonstrates that a convolutional neural network can overcome these challenges to learn successful control policies from raw video data in complex RL environments. The network is trained with a variant of the Q-learning [26] algorithm, with stochastic gradient descent to update the weights.

This paper proposes a new multi-stage TNEP method based on the deep Q-network (DQN) algorithm, which can solve the multi-stage TNEP problem based on a static TNEP model. The main purpose of this research is to provide grid planners with a simple and effective multi-stage TNEP method, whichDiving into the atari-game playing algorithm - Deep Q-Networks. December 01, 2019. This was a small collaborative research project about the convergence properties of the Deep Q Network for the MSc Reinforcement Learning course at the University of Amsterdam. Written by Leon Lang, Igor Pejic, Simon Passenheim, and Yoni Schirris.Policy Gradient (DDPG) algorithm[2]. The research aims to determine if and or when there are distinct advantages to using discrete or continuous action spaces when designing new DRL problems and algorithms. In this work we present preliminary results for both the DQN and DDPG algorithms to a known RL problem of the LunarLander using OpenAI Gym[1].

Oct 12, 2021 · In this paper, we propose a reinforcement learning (RL)-based framework for UAV-BS beam alignment using deep Q-Network (DQN) in a mmWave setting. We consider uplink communications where the UAV hovers around 5G new radio (NR) BS coverage area, with varying channel conditions. The proposed learning framework uses the location information to ... Diving into the atari-game playing algorithm - Deep Q-Networks. December 01, 2019. This was a small collaborative research project about the convergence properties of the Deep Q Network for the MSc Reinforcement Learning course at the University of Amsterdam. Written by Leon Lang, Igor Pejic, Simon Passenheim, and Yoni Schirris.AB - Purpose This paper aims to use the Monodepth method to improve the prediction speed of identifying the obstacles and proposes a Probability Dueling DQN algorithm to optimize the path of the agent, which can reach the destination more quickly than the Dueling DQN algorithm.

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In addition, the authors also showed that Thompson sampling can work with bootstrapped DQN reinforcement learning algorithm. For validation, the authors tested the proposed algorithm on various Atari benchmark gaming suites. This paper tries to use a Thompson sampling like approach to make decisions. Thompson Sampling