Batch Mode Reinforcement Learning based on the Synthesis of Artificial Trajectories

Area of Science:

• Artificial intelligence
• Machine learning
• Control theory

Background:

• Batch mode reinforcement learning (RL) aims to derive optimal policies from pre-collected trajectory data.
• Continuous state-space problems in RL typically require function approximators for control or value functions.
• Existing methods face challenges in efficiently learning from limited or discrete trajectory samples.

Purpose of the Study:

• To propose an alternative to function approximators in batch mode reinforcement learning for continuous state spaces.
• To introduce the concept of synthesizing "artificial trajectories" from existing data.
• To demonstrate the potential of this new approach for designing and analyzing RL algorithms.

Main Methods:

• Focus on the batch mode reinforcement learning setting with continuous state spaces.
• Synthesize "artificial trajectories" using the provided sample of trajectories.
• Develop and analyze new algorithms based on the artificial trajectory concept.

Main Results:

• Demonstrated that artificial trajectories can be effectively synthesized from sample data.
• Showcased that this method provides a viable alternative to traditional function approximation techniques.
• Opened new research avenues for designing and analyzing batch mode reinforcement learning algorithms.

Conclusions:

• The synthesis of artificial trajectories presents a promising direction for advancing batch mode reinforcement learning.
• This technique offers a novel perspective for overcoming limitations associated with function approximators in continuous state spaces.
• Further research into artificial trajectory generation can lead to more robust and efficient RL algorithms.