Robot See, Robot Do: Imitation Reward for Noisy Financial Environments

• URL: http://arxiv.org/abs/2411.08637v1
• Date: Wed, 13 Nov 2024 14:24:47 GMT
• Title: Robot See, Robot Do: Imitation Reward for Noisy Financial Environments
• Authors: Sven Goluža, Tomislav Kovačević, Stjepan Begušić, Zvonko Kostanjčar,
• Abstract summary: This paper introduces a novel and more robust reward function by leveraging imitation learning. We integrate imitation (expert's) feedback with reinforcement (agent's) feedback in a model-free reinforcement learning algorithm. Empirical results demonstrate that this novel approach improves financial performance metrics compared to traditional benchmarks.
• Score: 0.0
• License:
• Abstract: The sequential nature of decision-making in financial asset trading aligns naturally with the reinforcement learning (RL) framework, making RL a common approach in this domain. However, the low signal-to-noise ratio in financial markets results in noisy estimates of environment components, including the reward function, which hinders effective policy learning by RL agents. Given the critical importance of reward function design in RL problems, this paper introduces a novel and more robust reward function by leveraging imitation learning, where a trend labeling algorithm acts as an expert. We integrate imitation (expert's) feedback with reinforcement (agent's) feedback in a model-free RL algorithm, effectively embedding the imitation learning problem within the RL paradigm to handle the stochasticity of reward signals. Empirical results demonstrate that this novel approach improves financial performance metrics compared to traditional benchmarks and RL agents trained solely using reinforcement feedback.

Related papers

• Enhancing Spectrum Efficiency in 6G Satellite Networks: A GAIL-Powered Policy Learning via Asynchronous Federated Inverse Reinforcement Learning [67.95280175998792]
  A novel adversarial imitation learning (GAIL)-powered policy learning approach is proposed for optimizing beamforming, spectrum allocation, and remote user equipment (RUE) association ins. We employ inverse RL (IRL) to automatically learn reward functions without manual tuning. We show that the proposed MA-AL method outperforms traditional RL approaches, achieving a $14.6%$ improvement in convergence and reward value.
  arXiv  Detail & Related papers  (2024-09-27T13:05:02Z)
• Provable Risk-Sensitive Distributional Reinforcement Learning with General Function Approximation [54.61816424792866]
  We introduce a general framework on Risk-Sensitive Distributional Reinforcement Learning (RS-DisRL), with static Lipschitz Risk Measures (LRM) and general function approximation. We design two innovative meta-algorithms: textttRS-DisRL-M, a model-based strategy for model-based function approximation, and textttRS-DisRL-V, a model-free approach for general value function approximation.
  arXiv  Detail & Related papers  (2024-02-28T08:43:18Z)
• A Neuromorphic Architecture for Reinforcement Learning from Real-Valued Observations [0.34410212782758043]
  Reinforcement Learning (RL) provides a powerful framework for decision-making in complex environments. This paper presents a novel Spiking Neural Network (SNN) architecture for solving RL problems with real-valued observations.
  arXiv  Detail & Related papers  (2023-07-06T12:33:34Z)
• Provable Reward-Agnostic Preference-Based Reinforcement Learning [61.39541986848391]
  Preference-based Reinforcement Learning (PbRL) is a paradigm in which an RL agent learns to optimize a task using pair-wise preference-based feedback over trajectories. We propose a theoretical reward-agnostic PbRL framework where exploratory trajectories that enable accurate learning of hidden reward functions are acquired.
  arXiv  Detail & Related papers  (2023-05-29T15:00:09Z)
• Internally Rewarded Reinforcement Learning [22.01249652558878]
  We study a class of reinforcement learning problems where the reward signals for policy learning are generated by an internal reward model. We show that the proposed reward function can consistently stabilize the training process by reducing the impact of reward noise.
  arXiv  Detail & Related papers  (2023-02-01T06:25:46Z)
• Backward Imitation and Forward Reinforcement Learning via Bi-directional Model Rollouts [11.4219428942199]
  Traditional model-based reinforcement learning (RL) methods generate forward rollout traces using the learnt dynamics model. In this paper, we propose the backward imitation and forward reinforcement learning (BIFRL) framework. BIFRL empowers the agent to both reach to and explore from high-value states in a more efficient manner.
  arXiv  Detail & Related papers  (2022-08-04T04:04:05Z)
• Stock Trading Optimization through Model-based Reinforcement Learning with Resistance Support Relative Strength [4.322320095367326]
  We design a new approach that leverages resistance and support (RS) level as regularization terms for action in model-based reinforcement learning (MBRL) algorithms. Our proposed method even resists big drop (less maximum drawdown) during COVID-19 pandemic period when the financial market got unpredictable crisis.
  arXiv  Detail & Related papers  (2022-05-30T12:36:48Z)
• Reward Uncertainty for Exploration in Preference-based Reinforcement Learning [88.34958680436552]
  We present an exploration method specifically for preference-based reinforcement learning algorithms. Our main idea is to design an intrinsic reward by measuring the novelty based on learned reward. Our experiments show that exploration bonus from uncertainty in learned reward improves both feedback- and sample-efficiency of preference-based RL algorithms.
  arXiv  Detail & Related papers  (2022-05-24T23:22:10Z)
• Distributional Reinforcement Learning for Multi-Dimensional Reward Functions [91.88969237680669]
  We introduce Multi-Dimensional Distributional DQN (MD3QN) to model the joint return distribution from multiple reward sources. As a by-product of joint distribution modeling, MD3QN can capture the randomness in returns for each source of reward. In experiments, our method accurately models the joint return distribution in environments with richly correlated reward functions.
  arXiv  Detail & Related papers  (2021-10-26T11:24:23Z)
• Balancing Value Underestimation and Overestimation with Realistic Actor-Critic [6.205681604290727]
  This paper introduces a novel model-free algorithm, Realistic Actor-Critic(RAC), which can be incorporated with any off-policy RL algorithms to improve sample efficiency. RAC employs Universal Value Function Approximators (UVFA) to simultaneously learn a policy family with the same neural network, each with different trade-offs between underestimation and overestimation. We evaluate RAC on the MuJoCo benchmark, achieving 10x sample efficiency and 25% performance improvement on the most challenging Humanoid environment compared to SAC.
  arXiv  Detail & Related papers  (2021-10-19T03:35:01Z)
• Combining Pessimism with Optimism for Robust and Efficient Model-Based Deep Reinforcement Learning [56.17667147101263]
  In real-world tasks, reinforcement learning agents encounter situations that are not present during training time. To ensure reliable performance, the RL agents need to exhibit robustness against worst-case situations. We propose the Robust Hallucinated Upper-Confidence RL (RH-UCRL) algorithm to provably solve this problem.
  arXiv  Detail & Related papers  (2021-03-18T16:50:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.