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RLM (short for Reinforcement Learning Models) is a modular framework that makes it easier to build, train, evaluate, and deploy reinforcement learning (RL) agents across a wide range of environments and tasks. It provides a consistent API that abstracts away many of the repetitive engineering patterns in RL research and application work, letting developers focus on modeling, experimentation, and fine-tuning rather than infrastructure plumbing. Within the framework, you can define custom agents, environments, policy networks, and reward structures while leveraging built-in dataset utilities, logging, and checkpointing for reproducible experiments. RLM also includes integration with popular simulation environments and benchmark suites, giving researchers a ready-made playground for algorithm comparison and performance tracking.
Features
- Unified API for defining and running reinforcement learning agents
- Modular components for composable pipelines
- Integrations with benchmark environments and simulators
- Support for distributed and multi-GPU training
- Built-in logging, checkpointing, and evaluation tools
- Configurable reward, policy, and replay buffer abstractions
Project Samples
