MP-DQN improves deep Q-learning for complex action spaces.
problem Learning with discrete actions and continuous parameters in reinforcement learning.
method Multi-pass deep Q-networks (MP-DQN) to handle parameterised actions.
result Significantly outperforms P-DQN and other methods in data efficiency and policy performance.
Deep RL applied for Indian stock trading strategies.
problem Designing profitable trading strategies for Indian stock markets.
method Applied deep reinforcement learning to ten Indian stock datasets.
result Models' performance compared and evaluated.
Proposes an amortized variational framework for Deep Q Networks.
problem Efficient exploration in deep reinforcement learning.
method Amortized variational inference for action value function approximation.
result Significantly less learning parameters and better performance.
DQN outperforms traditional stock market strategies by 30%.
problem Optimizing portfolio management in the stock market.
method Deep Q-Network applied to portfolio management, with discretization and neural network enhancements.
result DQN strategy yields 30% higher profit and lower risk compared to traditional strategies.
We propose a framework that directly tackles the probability distribution of the value function parameters in Deep Q Network (DQN), with powerful variational inference subroutines to approximate the posterior of the parameters. We will establish the equivalence between our proposed surrogate objective and variational i…
Deep Recurrent Q-Network improves autonomous driving in urban areas with pedestrians.
problem Challenges in urban autonomous driving due to complex road structures and unpredictable pedestrian behavior.
method Combines Deep Q-Network with LSTM for long-term memory, designed a 3-D state representation, and uses a reward function.
result The proposed DRQN-based approach outperforms rule-based methods in dense urban scenarios.
In industrial environments, an increasing amount of wireless devices are used, which utilize license-free bands. As a consequence of these mutual interferences of wireless systems might decrease the state of coexistence. Therefore, a central coexistence management system is needed, which allocates conflict-free resourc…
Enhanced DQN model boosts trading performance with advanced techniques.
problem Improving automated trading performance in financial markets.
method Incorporation of Prioritized Experience Replay, Regularized Q-Learning, Noisy Networks, Dueling, Double DQN, and CNN architectures.
result Significantly improved returns and Sharpe Ratio compared to the original DQN model.
Auto-CASH uses Deep Q-Network to automatically select machine learning algorithms.
problem Automatic selection of machine learning algorithms and hyperparameters.
method Pre-trained model based on meta-learning using Deep Q-Network.
result Auto-CASH achieves better performance with shorter time compared to classical and state-of-the-art methods.
We study reinforcement learning (RL) in high dimensional episodic Markov decision processes (MDP). We consider value-based RL when the optimal Q-value is a linear function of d-dimensional state-action feature representation. For instance, in deep-Q networks (DQN), the Q-value is a linear function of the feature repres…
RS-DQN protects RL agents from adversarial attacks.
problem Adversarial attacks can disrupt deep RL training and evaluation.
method Online robustness training with RS-DQN combining state-of-the-art adversarial and provably robust training.
result RS-DQN makes RL agents resilient to strong attacks.
Enhances exploration in hierarchical networks using mutual information.
problem Limited exploration in hierarchical Deep Q-Networks.
method Adversarial Soft Actor-Critic with mutual information optimization.
result Improves hierarchical network exploration through mutual information maximization.
Deep Q-Network predicts global stock market returns from chart images.
problem Predicting global stock market returns using chart images.
method Deep Q-Network with CNN approximator, trained on US stock market, tested on 31 countries.
result Artificial intelligence can predict stock prices in small markets.
We methodologically address the problem of Q-value overestimation in deep reinforcement learning to handle high-dimensional state spaces efficiently. By adapting concepts from information theory, we introduce an intrinsic penalty signal encouraging reduced Q-value estimates. The resultant algorithm encompasses a wide r…
DQNs can approximate optimal Q-functions with high accuracy on compact sets.
problem Approximating optimal Q-functions in continuous-time Markov Decision Processes.
method Stochastic control, FBSDEs, residual network approximation theorems, large deviation bounds, viscosity solutions.
result DQNs can approximate optimal Q-functions on compact sets with arbitrary accuracy and high probability.
Adaptive traffic control uses deep RL to improve decision-making.
problem Improving traffic control using deep RL.
method Integrates recent deep RL techniques into a novel DQN-based algorithm (TC-DQN+) for traffic control.
result Proposes a new reward function for traffic control.
AI algorithms outperform traditional trading methods in stock markets.
problem Traditional trading methods struggle with risk management and edge over classical approaches.
method Used Deep Reinforcement Learning (DRL) algorithms (DDQN and PPO) to compare with Buy and Hold benchmark.
result DRL algorithms provide a substantial edge over classical approaches in terms of risk-adjusted returns.
Deep learning improves reinforcement learning in video games.
problem Challenges in reinforcement learning with high-dimensional inputs.
method Used deep Q-network and batch normalization to improve reinforcement learning.
result Some agents learned to play T-rex Runner better than human experts.
Deep Reinforcement Learning (RL) recently emerged as one of the most competitive approaches for learning in sequential decision making problems with fully observable environments, e.g., computer Go. However, very little work has been done in deep RL to handle partially observable environments. We propose a new architec…
Paper uses deep Q-network to correct eye movement artifacts in OCT volumes.
problem Unsupervised correction of inter-frame misalignments in OCT volumes due to eye movement artifacts.
method Dueling deep Q-network trained to maximize reward signals based on intensity-based image similarity metrics.
result Average normalized mutual information and correlation coefficient of 0.985 and 0.914, respectively.
This paper uses deep reinforcement learning to automate electric transmission voltage control.
problem Automating voltage control in electric transmission systems.
method Deep reinforcement learning (DRL) applied to voltage control, with a novel DQN modification.
result DRL can automate voltage control at scale, but more research is needed.
This research introduces an autonomous robot navigation method using reinforcement learning.
problem Improving robot navigation in complex environments.
method Deep Q Network (DQN) and Proximal Policy Optimization (PPO) models for path planning and decision-making.
result The models enhance robot navigation ability and adaptive learning in unknown environments.
Reinforcement learning (RL) algorithms have made huge progress in recent years by leveraging the power of deep neural networks (DNN). Despite the success, deep RL algorithms are known to be sample inefficient, often requiring many rounds of interaction with the environments to obtain satisfactory performance. Recently,…
The deep reinforcement learning method usually requires a large number of training images and executing actions to obtain sufficient results. When it is extended a real-task in the real environment with an actual robot, the method will be required more training images due to complexities or noises of the input images, …
Most existing deep reinforcement learning (DRL) frameworks consider either discrete action space or continuous action space solely. Motivated by applications in computer games, we consider the scenario with discrete-continuous hybrid action space. To handle hybrid action space, previous works either approximate the hyb…
A new method learns action representations for reinforcement learning.
problem Efficient action-value estimation in reinforcement learning.
method Action hypergraph networks framework for learning action representations.
result Hypergraph Q-networks show effectiveness on various domains.
A simple DQN-based multi-agent RL system for binary actions.
problem Complexity and training issues in multi-agent reinforcement learning.
method Shared state and rewards, agent-specific actions, experience replay pool.
result Better performance and faster convergence compared to conventional methods.
New algorithms tackle multi-agent problems with hybrid action spaces.
problem Applying deep reinforcement learning to multi-agent problems with discrete-continuous hybrid action spaces.
method Proposed two novel algorithms: Deep MAPQN and Deep MAHHQN, using centralized training and decentralized execution.
result Empirical results show both algorithms significantly outperform existing methods.
A deep reinforcement learning method with rule-based constraints improves safe and efficient lane changes in autonomous driving.
problem Complex and uncertain traffic environment challenges autonomous driving decision-making.
method Deep Q-Network (DQN) combined with rule-based constraints for lane change decision-making.
result The proposed rule-based DQN method outperforms both rule-based and DQN approaches in a real-world simulator.
New model-free reinforcement learning methods outperform traditional scheduling in cancer chemotherapy.
problem Optimal scheduling of cancer chemotherapy in the presence of uncertainty.
method Used Deep Q-Network (DQN) and Deep Deterministic Policy Gradient (DDPG) algorithms.
result DDPG outperforms DQN in eradicating cancer, suggesting continuous action space advantages.
Paper uses DDQN for trading assets, showing better performance than market benchmarks.
problem Improving financial trading strategies using AI.
method Double Deep Q-Network (DDQN) algorithm for trading multiple assets.
result Trading agent outperformed market benchmarks and achieved higher net asset value.
Combines Hebbian and DQN for better POMDP problem solving.
problem Difficult POMDP problems with TD errors.
method Modulated Hebbian plus Q network architecture (MOHQA) integrating Hebbian and DQN.
result Improved DQN performance and outperformed other algorithms on some POMDPs.
Improved robustness of RL policies in SNNs for Atari games.
problem High sensitivity of RL policies to noisy input data.
method Conversion of Q-Learning NNs trained using RL to Spiking Neural Networks (SNNs).
result Improved robustness of SNNs to occlusion in input images.
Deep reinforcement learning improves trading performance in financial markets.
problem Improving trading performance in financial markets.
method Deep Q-network (DQN) for designing long-short trading strategies.
result Trained reinforcement learning agent outperformed an index benchmark in trading E-mini S&P 500 futures contracts.
This paper improves radar performance against jammers using RL.
problem Improving radar performance against jammers.
method Reinforcement Learning (RL) with Deep Q-Network (DQN) and Long Short Term Memory (LSTM) networks.
result Softmax operator improves RL algorithm performance.
Deep RL model optimizes pedestrian evacuation in multi-exit scenarios.
problem Optimizing pedestrian evacuation in multi-exit indoor environments.
method MultiExit-DRL using Deep Reinforcement Learning with DQN and DNN.
result MultiExit-DRL reduces evacuation frames and optimizes exit utilization.
Proposes a deep RL framework for optimizing task allocation in crowdsourcing platforms.
problem Optimizing task allocation in crowdsourcing platforms for both workers and requesters.
method Uses Deep Q-Network (DQN) to estimate long-term rewards, considering immediate and future rewards simultaneously.
result Demonstrates superior performance in synthetic and real datasets.
Recently deep reinforcement learning (DRL) has achieved outstanding success on solving many difficult and large-scale RL problems. However the high sample cost required for effective learning often makes DRL unaffordable in resource-limited applications. With the aim of improving sample efficiency and learning performa…
A new method prioritizes and recycles experiences for better reinforcement learning.
problem Improving reinforcement learning efficiency by prioritizing and recycling experiences.
method Double-prioritized state-recycled (DPSR) experience replay.
result DPSR achieved state-of-the-art results in Atari games, outperforming original and prioritized methods.
Deep reinforcement learning (DRL) methods such as the Deep Q-Network (DQN) have achieved state-of-the-art results in a variety of challenging, high-dimensional domains. This success is mainly attributed to the power of deep neural networks to learn rich domain representations for approximating the value function or pol…
For a safe, natural and effective human-robot social interaction, it is essential to develop a system that allows a robot to demonstrate the perceivable responsive behaviors to complex human behaviors. We introduce the Multimodal Deep Attention Recurrent Q-Network using which the robot exhibits human-like social intera…
Deep RL solves combinatorial selection problems with large item spaces.
problem Solving MDPs with large state and action spaces, especially for combinatorial selection.
method Convert S-MDP to IS-MDP, use weight-shared Q-networks to manage state space explosion.
result Our approach effectively handles large item spaces and scales to diverse environments.
Proposes a deep reinforcement learning framework for dynamic multichannel access.
problem Efficient use of limited spectral resources in dynamic multichannel access.
method Deep actor-critic reinforcement learning framework for both single-user and multi-user scenarios.
result Demonstrates improved performance and adaptive ability compared to existing methods.
This review explores how multiple agents learn to communicate in complex environments.
problem Learning effective communication strategies among multiple agents in partially observable environments.
method Review of recent algorithms and models for improving communication between agents, including Deep Recurrent Q-Networks.
result Introduction of a novel entropy-based evaluation metric for communication strategies.
The deep Q-network (DQN) and return-based reinforcement learning are two promising algorithms proposed in recent years. DQN brings advances to complex sequential decision problems, while return-based algorithms have advantages in making use of sample trajectories. In this paper, we propose a general framework to combin…
New hybrid model combines GARCH and reinforcement learning for improved VaR estimation.
problem Inaccurate VaR estimation in volatile financial markets.
method Combines GARCH volatility models with DDQN reinforcement learning for dynamic risk forecasting.
result Significant improvement in VaR accuracy and reduction in breaches.
Online random forests improve Q-learning performance in specific gym environments.
problem Improving Q-learning performance in reinforcement learning tasks.
method Proposed online random forests as Q-function approximators and growing them as learning progresses.
result Improved performance over state-of-the-art Deep Q-Networks in specific gym environments.
A new method for learning Gaussian Mixture Models using gradient descent.
problem Learning Gaussian Mixture Models efficiently and with gradient descent.
method Sliced Cramér 2-distance for multivariate GMMs, compatible with gradient descent.
result Closed-form solution for univariate case, easy to implement.