Rainbow: Combining Improvements in Deep Reinforcement Learning [1].
Results and pretrained models can be found in the releases.
- DQN [2]
- Double DQN [3]
- Prioritised Experience Replay [4]
- Dueling Network Architecture [5]
- Multi-step Returns [6]
- Distributional RL [7]
- Noisy Nets [8]
Data-efficient Rainbow [9] can be run using the following options:
python main.py --target-update 2000 \
--T-max 100000 \
--learn-start 1600 \
--memory-capacity 100000 \
--replay-frequency 1 \
--multi-step 20 \
--architecture data-efficient \
--hidden-size 256 \
--learning-rate 0.0001 \
--evaluation-interval 10000
To install all dependencies with Anaconda run conda env create -f environment.yml
and use source activate rainbow
to activate the environment.
Available Atari games can be found in the atari-py
ROMs folder.
- @floringogianu for categorical-dqn
- @jvmancuso for Noisy layer
- @jaara for AI-blog
- @openai for Baselines
- @mtthss for implementation details
[1] Rainbow: Combining Improvements in Deep Reinforcement Learning
[2] Playing Atari with Deep Reinforcement Learning
[3] Deep Reinforcement Learning with Double Q-learning
[4] Prioritized Experience Replay
[5] Dueling Network Architectures for Deep Reinforcement Learning
[6] Reinforcement Learning: An Introduction
[7] A Distributional Perspective on Reinforcement Learning
[8] Noisy Networks for Exploration
[9] When to Use Parametric Models in Reinforcement Learning?