Random Network Distillation (RND)

Overview

RND is an exploration bonus for RL methods that's easy to implement and enables significant progress in some hard exploration Atari games such as Montezuma's Revenge. We use Proximal Policy Gradient as our RL method as used by original paper's implementation

Original paper:

• Exploration by Random Network Distillation

Our single-file implementations of RND:

• ppo_rnd_envpool.py
  • Uses the blazing fast Envpool vectorized environment.
  • 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 Discerete action space

Warning

Note that ppo_rnd_envpool.py does not work in Windows and MacOs . See envpool's built wheels here: https://pypi.org/project/envpool/#files

Implemented Variants

Variants Implemented	Description
ppo_rnd_envpool.py, docs	For continuous action space

Below are our single-file implementations of TD3:

ppo_rnd_envpool.py

Usage

poetry install --with envpool
python cleanrl/ppo_rnd_envpool.py --help
python cleanrl/ppo_rnd_envpool.py --env-id MontezumaRevenge-v5

Explanation of the logged metrics

See related docs for ppo.py. Below is the additional metric for RND:

• charts/episode_curiosity_reward: episodic intrinsic rewards.
• losses/fwd_loss: the prediction error between predict network and target network, can also be viewed as a proxy of the curiosity reward in that batch.

Implementation details

ppo_rnd_envpool.py uses a customized RecordEpisodeStatistics to work with envpool but has the same other implementation details as ppo_atari.py (see related docs). Additionally, it has the following additional details:

1. We initialize the normalization parameters by stepping a random agent in the environment by args.num_steps * args.num_iterations_obs_norm_init. args.num_iterations_obs_norm_init=50 comes from the original implementation.
2. We uses sticky action from envpool to facilitate the exploration like done in the original implementation.

Experiment results

To run benchmark experiments, see benchmark/rnd.sh. Specifically, execute the following command:

Below are the average episodic returns for ppo_rnd_envpool.py. To ensure the quality of the implementation, we compared the results against openai/random-network-distillation' PPO.

Environment	ppo_rnd_envpool.py	(Burda et al., 2019, Figure 7)1 2000M steps
MontezumaRevengeNoFrameSkip-v4	7100 (1 seed)	8152 (3 seeds)

Note the MontezumaRevengeNoFrameSkip-v4 has same setting to MontezumaRevenge-v5. Our benchmark has one seed due to limited compute resource and extreme long run time (~250 hours).

Learning curves:

Tracked experiments and game play videos:

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1. Burda, Yuri, et al. "Exploration by random network distillation." Seventh International Conference on Learning Representations. 2019. ↩