cleanrl/docs/rl-algorithms/rainbow.md

Rainbow

Overview

The Rainbow algorithm is an extension of DQN that combines multiple improvements:

• Prioritized Experience Replay
• Dueling Network Architecture
• Noisy Networks
• Distributional Q-Learning
• N-step Learning
• Double Q-Learning

Original papers:

• Rainbow: Combining Improvements in Deep Reinforcement Learning

Reference resources:

• :material-github: Dopamine

• :material-github: Kaixhin

Implemented Variants

Variants Implemented	Description
:material-github: rainbow_atari.py, :material-file-document: docs	For playing Atari games. It uses convolutional layers and common atari-based pre-processing techniques.

rainbow_atari.py

The rainbow_atari.py has the following features:

• For playing Atari games. It uses convolutional layers and common atari-based pre-processing techniques.
• Works with the Atari's pixel Box observation space of shape (210, 160, 3)
• Works with the Discrete action space

Usage

poetry install -E atari
python cleanrl/rainbow_atari.py --env-id BreakoutNoFrameskip-v4
python cleanrl/rainbow_atari.py --env-id PongNoFrameskip-v4

=== "poetry"

```bash
poetry install -E atari
poetry run python cleanrl/rainbow_atari.py --env-id BreakoutNoFrameskip-v4
poetry run python cleanrl/rainbow_atari.py --env-id PongNoFrameskip-v4
```

=== "pip"

```bash
pip install -r requirements/requirements-atari.txt
python cleanrl/rainbow_atari.py --env-id BreakoutNoFrameskip-v4
python cleanrl/rainbow_atari.py --env-id PongNoFrameskip-v4
```

Explanation of the logged metrics

Running python cleanrl/rainbow_atari.py will automatically record various metrics such as actor or value losses in Tensorboard. Below is the documentation for these metrics:

• charts/episodic_return: episodic return of the game
• charts/SPS: number of steps per second
• losses/td_loss: the n-step distributional TD loss
• losses/q_values: the mean Q values of the sampled data in the replay buffer
• charts/beta: the beta value of the prioritized experience replay

Implementation details

rainbow_atari.py is based on (Hessel et al., 2018)[^1], and uses the same hyperparameters as (Hessel et al., 2018)[^1]. See Table 1 in (Hessel et al., 2018)[^1] for the hyperparameters. However, there are a few implementation differences:

1. rainbow_atari.py uses the more popular Adam Optimizer with the --learning-rate=0.0000625 as follows:

   optim.Adam(q_network.parameters(), lr=0.0000625)
   

   whereas (Hessel et al., 2018)[^1] uses the RMSProp optimizer with --learning-rate=0.0000625, gradient momentum 0.95, squared gradient momentum 0.95, and min squared gradient 0.01 as follows:

   optim.RMSprop(
       q_network.parameters(),
       lr=2.5e-4,
       momentum=0.95,
       # ... PyTorch's RMSprop does not directly support
       # squared gradient momentum and min squared gradient
       # so we are not sure what to put here.
   )
   

Experiment results

To run benchmark experiments, see :material-github: benchmark/rainbow.sh. Specifically, execute the following command:

--8<-- "benchmark/rainbow.sh:0:6"

Below are the average episodic returns for rainbow_atari.py.

	Rainbow	C51	DQN
AlienNoFrameskip-v4	2907.03 ± 355.53	1831.00 ± 98.23	1275.77 ± 65.41
AssaultNoFrameskip-v4	7661.11 ± 226.51	3322.54 ± 94.46	3845.70 ± 443.31
GopherNoFrameskip-v4	8111.07 ± 300.60	8715.60 ± 492.23	10415.53 ± 3438.12
YarsRevengeNoFrameskip-v4	63536.39 ± 5432.22	11010.99 ± 904.27	15290.12 ± 8010.56
SpaceInvadersNoFrameskip-v4	1835.52 ± 205.10	2009.05 ± 226.96	1441.68 ± 23.92
MsPacmanNoFrameskip-v4	3113.30 ± 393.00	2445.13 ± 30.16	2109.43 ± 49.85

Learning curves:

Rainbow shows better performance than C51 and DQN.

Rainbow is also more sample efficient than C51 and DQN.

Rainbow obtains better aggregated performance than C51 and DQN.

[^1]: Hessel, M., Modayil, J., Hasselt, H.V., Schaul, T., Ostrovski, G., Dabney, W., Horgan, D., Piot, B., Azar, M.G., & Silver, D. (2018). Rainbow: Combining Improvements in Deep Reinforcement Learning. AAAI.