Agentsrl

Agentsrl is a term used in AI and reinforcement learning to denote software that implements autonomous agents driven by reinforcement-learning algorithms. It is not the name of a single widely recognized product, but rather a generic label used in tutorials, academic writing, and open-source projects to describe the components and workflow involved in training agents.

A typical agentsrl-style framework provides an environment interface compatible with common benchmarks, a set of agent implementations (including value-based, policy-gradient, and actor-critic methods), and a training loop that alternates between environment interaction and learning updates. The design emphasizes modularity so researchers can swap environments, policies, and optimizers without rewriting core logic. Common features include configuration management, reproducibility measures such as fixed random seeds, and tooling for logging, evaluation, and visualization of learning performance.

In educational and research contexts, agentsrl serves as a conceptual or practical starting point for experiments in exploration strategies, sample efficiency, transfer learning, and multi-agent coordination. Because the term is widely used in informal discussions, it may refer to a concrete library in some cases or simply to the general practice of building reinforcement-learning agents.

There is no single authoritative project named agentsrl, and the term may appear in diverse forms across repositories and notebooks. When encountered, its meaning should be inferred from the surrounding context to determine whether it references a specific implementation, a teaching example, or a broader methodological concept.