| Recurrent Deterministic Policy Gradient (RDPG) |
Overview ====== PyTorch implementation of Recurrent Deterministic Policy Gradient from the paper Memory-based control with recurrent neural networks
- Requirements
$ python -m pip install --upgrade pip $ pip install -r requirements.txt
Training:
- Pendulum-v0
$ python main.py --env Pendulum-v0 --max_episode_length 1000 --trajectory_length 10 --debug- Testing (TODO)
References: ====== * Memory-based control with recurrent neural networks <https://arxiv.org/abs/1512.04455> * Continuous control with deep reinforcement learning <https://arxiv.org/abs/1509.02971> * DDPG implementation using PyTorch <https://github.com/ghliu/pytorch-ddpg> * PyTorch-RL <https://github.com/jingweiz/pytorch-rl>