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TENSORFORCE
TensorFlow library for applied reinforcement learning
Apache-2.0
ABOUT
Applied RL in TensorFlow often means gluing paper implementations that are not configurable for production environments. Tensorforce exposes modular agents, memories, and execution modes so teams can specify DQN or PPO in config and run them against real control or recommendation tasks.
INSTALL
pip install tensorforceINTEGRATION GUIDE
1. Configure a PPO or DQN agent in Tensorforce and train it on a Gymnasium env
2. Deploy an applied RL agent where TensorFlow serving is already the stack
3. Swap replay, exploration, and network modules without rewriting the trainer
4. Prototype a production control policy with a config-driven TensorFlow agent
TAGS
pythontensorflowreinforcement-learningppodqnapplied-rl