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ELEGANTRL
Massively parallel deep reinforcement learning on GPUs
Apache-2.0
ABOUT
Standard RL trainers underuse the GPU because environments and updates run sequentially, so finance and robotics experiments take days. ElegantRL vectorizes environments and learners for massively parallel PPO, SAC, and DDPG training, cutting wall-clock time for large-batch policy optimization without rewriting the algorithm.
INSTALL
pip install ElegantRLINTEGRATION GUIDE
1. Train a PPO or SAC trading agent on vectorized market environments using GPU batching
2. Scale DDPG or TD3 on continuous-control tasks with ElegantRL's parallel sampler
3. Prototype a finance RL experiment from the helloworld notebooks before writing a custom env
4. Compare massively parallel ElegantRL runs against a single-process Stable Baselines3 baseline
TAGS
pythonpytorchreinforcement-learninggpufinancepposac