Enhances reinforcement learning from sparse data.
problem Limited data for offline reinforcement learning.
method Trajectory-based data augmentation.
result Improves reinforcement learning performance.
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
168,742 papers · 148 categories
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48 results for “positive reinforcement”
Paper uses SAC RL to optimize market-making strategies.
problem Optimizing market-making strategies with risk management.
method Applying SAC reinforcement learning to automate market-making decisions.
result Agent learns to optimize spreads and hedge trades.
Robotic grasping improved using evolutionary computing and deep reinforcement learning.
problem Developing a robot capable of grasping objects as skillfully as humans.
method Position estimation using Genetic Algorithm and regression, orientation learning using deep reinforcement learning.
result Deep reinforcement learning model outperforms traditional methods for orientation learning.
A new DRL model for intraday trading incorporating positional context.
problem Neglecting positional context in existing DRL intraday trading strategies.
method Introducing positional features into the state space of a DRL model.
result Significant improvement in profitability and risk-adjusted metrics.
In many platforms, user arrivals exhibit a self-reinforcing behavior: future user arrivals are likely to have preferences similar to users who were satisfied in the past. In other words, arrivals exhibit positive externalities. We study multiarmed bandit (MAB) problems with positive externalities. We show that the self…
In this paper we combine one method for hierarchical reinforcement learning - the options framework - with deep Q-networks (DQNs) through the use of different "option heads" on the policy network, and a supervisory network for choosing between the different options. We utilise our setup to investigate the effects of ar…
We adopt Deep Reinforcement Learning algorithms to design trading strategies for continuous futures contracts. Both discrete and continuous action spaces are considered and volatility scaling is incorporated to create reward functions which scale trade positions based on market volatility. We test our algorithms on the…
Recommender systems play a crucial role in mitigating the problem of information overload by suggesting users' personalized items or services. The vast majority of traditional recommender systems consider the recommendation procedure as a static process and make recommendations following a fixed strategy. In this paper…
The paper examines how slightly biasing towards under-represented groups in sequential selection processes can lead to long-term fairness.
problem Designing fair sequential decision-making processes for long-term social fairness.
method Proposes Multi-agent Fair-Greedy policy to balance score maximization and fairness.
result Proves convergence to long-term fairness target set by agents when score distributions are identical.
In reinforcement learning, a decision needs to be made at some point as to whether it is worthwhile to carry on with the learning process or to terminate it. In many such situations, stochastic elements are often present which govern the occurrence of rewards, with the sequential occurrences of positive rewards randoml…
Derives RL framework for systems without velocity or acceleration measurements.
problem Learning control for systems with limited sensor data.
method Gaussian Process Regression with a novel derivative-free kernel.
result Improved estimation performance and data-efficiency compared to traditional methods.
Optimizes trade execution with reinforcement learning for limit orders.
problem Maximizing revenue in a limit order book with market and limit orders.
method Formulated as a dynamic allocation task, uses multivariate logistic-normal distributions for efficient training.
result Outperforms traditional strategies in simulated environments.
Market makers play an important role in providing liquidity to markets by continuously quoting prices at which they are willing to buy and sell, and managing inventory risk. In this paper, we build a multi-agent simulation of a dealer market and demonstrate that it can be used to understand the behavior of a reinforcem…
Reinforcement learning has exceeded human-level performance in game playing AI with deep learning methods according to the experiments from DeepMind on Go and Atari games. Deep learning solves high dimension input problems which stop the development of reinforcement for many years. This study uses both two techniques t…
Continuous reinforcement learning such as DDPG and A3C are widely used in robot control and autonomous driving. However, both methods have theoretical weaknesses. While DDPG cannot control noises in the control process, A3C does not satisfy the continuity conditions under the Gaussian policy. To address these concerns,…
Myopic optimization outperforms reinforcement learning in portfolio management, leading to lower returns and higher risks.
problem Reinforcement learning strategies in portfolio management yield lower or negative returns and higher risks compared to myopic optimization.
method Modeling execution/liquidation frictions with mark-to-market accounting, using Malliavin calculus to derive policy gradients and risk shadow price, and quantifying phantom profit.
result Myopic optimization outperforms reinforcement learning in portfolio management, leading to better returns and lower risks.
Simple object representations improve model-free RL performance.
problem Current reinforcement learning agents lack object recognition.
method Used simple, feature-engineered object representations with the Rainbow model.
result Object representations significantly boost performance on Atari games.
Assisted by neural networks, reinforcement learning agents have been able to solve increasingly complex tasks over the last years. The simulation environment in which the agents interact is an essential component in any reinforcement learning problem. The environment simulates the dynamics of the agents' world and henc…
This paper proposes a trading strategy using TD3 for stock and cryptocurrency markets.
problem Predicting price movements in financial markets using historical data.
method Twin-Delayed DDPG (TD3) for continuous action space in algorithmic trading.
result The proposed strategy improves trading performance based on Return and Sharpe ratio metrics.
The paper tackles backtest overfitting in cryptocurrency trading using deep reinforcement learning.
problem Backtest overfitting in deep reinforcement learning for cryptocurrency trading.
method Formulated hypothesis test for overfitting detection, trained agents, estimated overfitting probability, and rejected overfitted agents.
result Less overfitted deep reinforcement learning agents outperformed more overfitted agents and market benchmarks.
Learning Force Control for Contact-rich Manipulation Tasks with Rigid Position-controlled Robotscs.LG
This work tackles force control for contact-rich manipulation tasks with rigid robots using RL.
problem Challenges in working with real robotic hardware, especially position-controlled robots.
method Combines RL with traditional force control techniques, implementing parallel position/force control and admittance control.
result Validated methods on both simulation and real robot (UR3 e-series) for force control.
Enhanced financial reward with shuffled feature CNN-DRL.
problem Improving reward in financial data using CNN-DRL.
method Applying shuffled features to financial data for CNN-DRL.
result Substantial enhancement in reward attainment.
Agent learns to trade currency pairs with improved risk management.
problem Improving systematic FX trading performance with online transfer learning.
method Online inductive transfer learning using feature representation from Gaussian mixture model to a reinforcement learning agent.
result Annualized portfolio information ratio of 0.52, compound return of 9.3%.
Paper tackles reinforcement learning with complex observations and simple latent dynamics.
problem Understanding reinforcement learning with complex observations and simple latent dynamics.
method Statistical and algorithmic analysis of reinforcement learning under general latent dynamics.
result Identifies latent pushforward coverability as a condition for statistical tractability.
Deep RL optimizes dynamic portfolio weights in China's stock market.
problem Traditional portfolio optimization methods struggle with dynamic asset weight adjustments.
method Developed a deep reinforcement learning framework with novel reward functions and random sampling.
result Model outperforms traditional methods in portfolio optimization and risk mitigation.
This paper uses DRL to optimize liquidity in DeFi protocols, making markets more accessible.
problem Optimizing liquidity provisioning in decentralized finance protocols.
method Modeling liquidity provisioning as an MDP, training an agent with PPO to dynamically adjust positions.
result DRL-based strategy outperforms traditional heuristics in fee maximization and impermanent loss mitigation.
In this paper, we propose a deep reinforcement learning (DRL) solution to the grasping problem using 2.5D images as the only source of information. In particular, we developed a simulated environment where a robot equipped with a vacuum gripper has the aim of reaching blocks with planar surfaces. These blocks can have …
Paper solves pendulum swing-up problem using RL.
problem Solving the classic pendulum swing-up problem.
method Deep Deterministic Policy Gradient algorithm applied to continuous action domain.
result Optimal pendulum achieved with increasing average return and decreasing loss.
Robotic navigation improves with RL and ultrasound images.
problem Improving accuracy in robotic navigation for ultrasound-guided procedures.
method Combines deep Q-networks with ultrasound images and binary classifier.
result Significantly improved navigation accuracy compared to pure RL and SL.
This paper analyzes DRL strategies in finance, revealing unique trading patterns and performance differences.
problem Limited research on DRL behavior in finance applications.
method Analysis of trading behaviors and purchase diversity of DRL algorithms (A2C, PPO, SAC, DDPG, TD3).
result DRL algorithms exhibit distinct trading patterns and performance differences, with A2C outperforming others in terms of cumulative rewards.
R3L uses planning algorithms to efficiently explore sparse reward environments.
problem Balancing exploration and exploitation in sparse reward reinforcement learning.
method Formulate exploration as a search problem using RRT, leverage demonstrations from initial solutions to refine RL policy.
result R3L outperforms classic and intrinsic exploration techniques, requiring fewer samples and achieving better asymptotic performance.
Attention models have had a significant positive impact on deep learning across a range of tasks. However previous attempts at integrating attention with reinforcement learning have failed to produce significant improvements. We propose the first combination of self attention and reinforcement learning that is capable …
Humans and animals solve a difficult problem much more easily when they are presented with a sequence of problems that starts simple and slowly increases in difficulty. We explore this idea in the context of reinforcement learning. Rather than providing the agent with an externally provided curriculum of progressively …
Paper uses RL to optimize derivative hedging with reduced costs.
problem Optimizing hedging strategies for derivatives with transaction costs.
method Reinforcement learning with two Q-functions, continuous state/action space, hybrid valuation model.
result Optimal hedging reduces mean and variance of hedging costs.
A minimalist approach improves LLM reasoning by filtering incorrect responses.
problem Improving large language model (LLM) reasoning on complex tasks.
method Revisit GRPO from a reinforce-like algorithm perspective, proposing Reinforce-Rej.
result RAFT, a simple rejection sampling baseline, outperforms GRPO and PPO.
DRL agents learn to trade Intel stock with stable positive returns.
problem Active high frequency trading in the stock market.
method End-to-end DRL framework using Proximal Policy Optimization, Sequential Model Based Optimization, and LOB-based meta-features.
result DRL agents create dynamic trading strategies with stable positive returns.
UAVs learn to collect data from IoT sensors efficiently.
problem Efficient UAV path planning for wireless data collection.
method Deep reinforcement learning (DDQN) with experience replay and convolutional layers.
result UAV control policy generalizes over changing scenario parameters.
Logarithmic regret achieved in Q-learning with positive gap.
problem Achieving logarithmic cumulative regret in Q-learning with positive sub-optimality gap.
method Optimistic Q-learning with logarithmic regret bound.
result Logarithmic cumulative regret bound proven for optimistic Q-learning.
Reinforcement Learning for Portfolio Optimization with a Financial Goal and Defined Time Horizonsq-fin.PM
Enhanced portfolio optimization for a financial goal with G-Learning and GIRL algorithms.
problem Maximize portfolio value while minimizing periodic contributions by a target date in volatile markets.
method Combines G-Learning and GIRL algorithms for dynamic portfolio optimization.
result Improved Sharpe Ratio from 0.42 to 0.483, demonstrating robust optimization in volatile markets.
Paper tackles multi-object reinforcement learning, improving skill extrapolation.
problem Learning robust manipulation tasks in multi-object settings.
method Introduces a linear relation network module to enhance skill generalization.
result Agents can extrapolate and generalize to any new object number, scaling linearly.
Paper proposes TDQN, a DRL strategy for optimal stock trading.
problem Optimal trading position determination in stock markets.
method Deep reinforcement learning (DRL) with Trading Deep Q-Network (TDQN) algorithm.
result TDQN strategy significantly improves Sharpe ratio performance.
Coordinated defensive escorts can aid a navigating payload by positioning themselves in order to maintain the safety of the payload from obstacles. In this paper, we present a novel, end-to-end solution for coordinating an escort team for protecting high-value payloads. Our solution employs deep reinforcement learning …
Mobile robot navigation in complex and dynamic environments is a challenging but important problem. Reinforcement learning approaches fail to solve these tasks efficiently due to reward sparsities, temporal complexities and high-dimensionality of sensorimotor spaces which are inherent in such problems. We present a nov…
Continuous-time reinforcement learning: ellipticity enables model-free value function approximationcs.LG
New algorithm learns value and advantage functions for continuous-time Markov processes without structural assumptions.
problem Learning value and advantage functions for continuous-time Markov processes without structural assumptions.
method Proposes Sobolev-prox fitted -learning algorithm based on Hilbert-space positive definiteness and boundedness properties of Bellman operators.
result Identifies ellipticity as a key structural property enabling reinforcement learning for Markov diffusions.
A new method for robot manipulation tasks using imagined object goals.
problem Learning robot manipulation in sparse reward environments.
method Train objects to reach target positions, then use predictions to create a curriculum of tasks.
result Higher success rates in challenging learning scenarios compared to alternatives.
New algorithms improve contextual bandit performance by adapting to problem difficulty.
problem Improving contextual bandit performance on problems with varying difficulty.
method Introducing complexity measures and oracle-efficient algorithms.
result Achieves optimal instance-dependent regret bounds for rich policy classes.
Improved hyperparameter optimization using simplified Transformer blocks.
problem Discovering optimal architectures in high-dimensional search spaces with limited exploration budgets.
method Simplified Transformer block for modeling hyper-parameter dependencies, actor-critic style algorithm, ensembling.
result Outperformed most algorithms on NAS-Bench-101 and Random Search in discovering more accurate model architectures.
Paper introduces TrufLL for language model training without labeled data.
problem Training language models from scratch with reinforcement learning.
method Dynamic vocabulary truncation in RL for language generation.
result TrufLL successfully learns language generation policies from scratch.