Sophisticated RL Models for Complex Systems

Current Trends in Reinforcement Learning for Complex Systems

Recent advancements in reinforcement learning (RL) are significantly pushing the boundaries of what is possible in controlling and optimizing complex systems. The field is witnessing a shift towards more sophisticated models that can handle non-Markovian dynamics, temporal logic objectives, and intricate reward structures. Innovations in RL algorithms are enabling the learning of optimal policies in environments with sparse rewards and uncertain dynamics, which is crucial for applications in healthcare, finance, and robotics.

One notable trend is the integration of temporal logic with RL, allowing for the specification and optimization of complex, long-term objectives. This approach not only enhances the interpretability of learned policies but also improves their robustness against uncertainties. Additionally, there is a growing emphasis on sample-efficient learning, with new methods leveraging task-specific knowledge to guide exploration and reduce the need for extensive data.

Another significant development is the mitigation of suboptimality in deterministic policy gradients, particularly in tasks with high-dimensional action spaces and complex Q-functions. By introducing novel actor architectures and surrogate Q-functions, researchers are making strides in overcoming local optima and achieving more consistent performance.

Noteworthy Papers:

  • Reinforcement Learning with LTL and $\omega$-Regular Objectives via Optimality-Preserving Translation to Average Rewards: Demonstrates a novel approach to translating $\omega$-regular objectives into average reward problems, preserving optimality and enhancing policy learning.
  • Sample-Efficient Reinforcement Learning with Temporal Logic Objectives: Leveraging the Task Specification to Guide Exploration: Introduces a task-driven exploration strategy that significantly accelerates learning in complex, sparse-reward environments.
  • Potential-Based Intrinsic Motivation: Preserving Optimality With Complex, Non-Markovian Shaping Rewards: Extends potential-based reward shaping to intrinsic motivation methods, ensuring optimal policy preservation in complex environments.

Sources

Reinforcement Learning for Control of Non-Markovian Cellular Population Dynamics

Synthesis from LTL with Reward Optimization in Sampled Oblivious Environments

Motion Planning for Automata-based Objectives using Efficient Gradient-based Methods

Mitigating Suboptimality of Deterministic Policy Gradients in Complex Q-functions

Reinforcement Learning with LTL and $\omega$-Regular Objectives via Optimality-Preserving Translation to Average Rewards

Sample-Efficient Reinforcement Learning with Temporal Logic Objectives: Leveraging the Task Specification to Guide Exploration

Potential-Based Intrinsic Motivation: Preserving Optimality With Complex, Non-Markovian Shaping Rewards

Verification of Linear Dynamical Systems via O-Minimality of Real Numbers

Deep Reinforcement Learning for Online Optimal Execution Strategies

Contracting With a Reinforcement Learning Agent by Playing Trick or Treat

ORSO: Accelerating Reward Design via Online Reward Selection and Policy Optimization

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