Inhibiting Error Exacerbation in Offline Reinforcement Learning With Data Sparsity.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning

Background:

• Offline reinforcement learning (RL) learns from fixed datasets, avoiding risky real-time interaction.
• Out-of-distribution (OOD) approximation errors can lead to performance degradation in offline RL.
• Data sparsity significantly impacts estimation errors, a factor often overlooked.

Purpose of the Study:

• To propose a novel offline RL approach, IEEDS, to inhibit error exacerbation caused by data sparsity.
• To develop a value estimation method that accounts for the influence of data sparsity.
• To improve the stability and performance of offline RL agents.

Main Methods:

• Implemented an offline RL approach (IEEDS) focusing on data sparsity.
• Introduced a novel value estimation method using V-nets instead of Q-nets for denser state spaces.
• Designed a state-aware-sparsity Markov decision process (MDP) to incorporate state sparsity into training.
• Theoretically proved the convergence of IEEDS under the proposed MDP framework.

Main Results:

• The IEEDS approach effectively inhibits error exacerbation by considering data sparsity.
• Using V-nets leads to more accurate value estimation due to concentrated data in smaller state spaces.
• The state-aware-sparsity MDP successfully lessens the impact of sparse states during training.
• Extensive experiments on offline RL benchmarks demonstrated IEEDS's superior performance compared to existing methods.

Conclusions:

• Data sparsity is a critical factor influencing estimation errors in offline RL.
• The proposed IEEDS method offers a robust solution for mitigating error exacerbation in offline RL.
• IEEDS enhances agent performance by effectively managing data sparsity and improving value estimation accuracy.