arXiv:2606.26463v1 Announce Type: new Abstract: Deliberating takes time. In real-time settings, that time is not free. Standard reinforcement learning (RL) sidesteps this as the environment waits indefinitely for the agent's decision. Instead, we study real-time RL environments where the environment progresses while waiting for the agent's action. Building on prior real-time formalizations, we introduce variable-delay real-time RL, where the agent chooses how long to deliberate at each decision point since the environment progresses. For the planning agents we use, the right delay is state-dep
The increasing complexity and real-world deployment of AI systems necessitate more efficient and adaptable decision-making processes, particularly in time-constrained environments.
This research addresses a critical limitation in current reinforcement learning, enabling AI agents to operate more effectively in dynamic, real-time scenarios where deliberation time has a cost.
AI agents will be able to dynamically allocate computational resources for planning based on immediate environmental conditions, leading to more responsive and robust autonomous systems.
- · AI agents developers
- · Robotics and autonomous systems
- · Real-time control systems
- · Gaming and simulation industries
- · AI systems with rigid, pre-defined planning cycles
- · Applications demanding instant, un-optimized AI responses
AI agents can adapt their decision-making latency to optimize performance under diverse real-time constraints.
This improved adaptability could accelerate the deployment of autonomous systems in complex, safety-critical environments.
More sophisticated real-time decision-making capabilities could lead to new paradigms in human-AI collaboration and autonomous system interaction.
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