google-deepmind/trfl

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

3130 stars · 384 forks observed · 2026-10-15

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

TRFL is a Python library built on TensorFlow that provides building-block loss operations (e.g., Q-learning, TD learning, distributional RL) for implementing reinforcement learning agents. It exposes differentiable loss tensors and auxiliary data via namedtuples that can be plugged into custom agent training loops.

Use cases

• implement q-learning agents in tensorflow
• compute td loss for rl training
• build custom reinforcement learning agents
• distributional rl loss functions
• implement sarsa and expected sarsa updates
• rl value function learning losses

When to choose

• you are building RL agents on TensorFlow and want well-tested loss primitives
• you need specific RL update rules like Q-learning, Retrace, or V-trace as differentiable ops

When to avoid

• you use PyTorch or JAX instead of TensorFlow
• you want a full agent framework rather than loss building blocks
• you need actively maintained software, as development has stalled

Facets

library · maturity maintenance

machine-learning reinforcement-learning deep-learning reinforcement-learning machine-learning deep-learning python tensorflow reinforcement-learning q-learning loss-functions deepmind agents gpu

Member repositories

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markdown · JSON · MCP: product_card(name="google-deepmind/trfl")