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Deep Reinforcement Learning from Human Preferences

Reproduction of OpenAI and DeepMind's Deep Reinforcement Learning from Human Preferences, based on the paper at


The main milestones of this reproduction were:

  • Training an agent to move the dot to the middle in a simple environment using synthetic preferences.

  • Training an agent to play Pong using synthetic preferences.

  • Training an agent to stay alongside other cars in Enduro using human preferences.


Python setup

This project uses Tensorflow 1, which needs Python 3.7 or below.

To set up an isolated environment and install dependencies, install Pipenv, then just run:

$ pipenv install

However, note that TensorFlow must be installed manually. Either:

$ pipenv run pip install tensorflow==1.15


$ pipenv run pip install tensorflow-gpu==1.15

depending on whether you have a GPU. (If you run into problems, try installing TensorFlow 1.6.0, which was used for development.)

If you want to run tests, also run:

$ pipenv install --dev

Finally, before running any of the scripts, enter the environment with:

$ pipenv shell


All training is done using Basic usage is:

$ python3 <mode> <environment>

Supported environments are MovingDotNoFrameskip-v0, PongNoFrameskip-v4, and EnduroNoFrameskip-v4.

Training with original rewards

To train using the original rewards from the environment rather than rewards based on preferences, use the train_policy_with_original_rewards mode.

For example, to train Pong:

$ python3 train_policy_with_original_rewards PongNoFrameskip-v4 --n_envs 16 --million_timesteps 10

Training end-to-end with preferences

Use the train_policy_with_preferences mode.

For example, to train MovingDotNoFrameskip-v0 using synthetic preferences:

$ python3 train_policy_with_preferences MovingDotNoFrameskip-v0 --synthetic_prefs --ent_coef 0.02 --million_timesteps 0.15

On a machine with a GPU, this takes about an hour. TensorBoard logs (created in a new directory in runs/ automatically) should look something like:

To train Pong using synthetic preferences:

$ python3 train_policy_with_preferences PongNoFrameskip-v4 --synthetic_prefs --dropout 0.5 --n_envs 16 --million_timesteps 20

On a 16-core machine without GPU, this takes about 13 hours. TensorBoard logs should look something like:

To train Enduro (a modified version with a time limit so the weather doesn't change, which the paper notes can confuse the reward predictor) using human preferences:

$ python3 train_policy_with_preferences EnduroNoFrameskip-v4 --n_envs 16 --render_episodes

You'll see two windows: a larger one showing a pair of examples of agent behaviour, and another smaller window showing the last full episode that the agent played (so you can see how qualitative behaviour is changing). Enter 'L' in the terminal to indicate that you prefer the left example; 'R' to indicate you prefer the right example; 'E' to indicate you prefer them both equally; and just press enter if the two clips are incomparable.

On an 8-core machine with GPU, it takes about 2.5 hours to reproduce the video above - about an hour to collect 500 preferences about behaviour from a random policy, then half an hour to pretrain the reward predictor using those 500 preferences, then an hour to train the policy (while still collecting preferences.)

The bottleneck is mainly labelling speed, so if you're already saved human preferences in runs/enduro, you can re-use those preferences by training with:

$ python3 train_policy_with_preferences EnduroNoFrameskip-v4 --n_envs 16 --render_episodes --load_prefs_dir runs/enduro --n_initial_epochs 10

This only takes about half an hour.

Piece-by-piece runs

You can also run different parts of the training process separately, saving their results for later use:

  • Use the gather_initial_prefs mode to gather the initial 500 preferences used for pretraining the reward predictor. This saves preferences to train_initial.pkl.gz and val_initial.pkl.gz in the run directory.
  • Use pretrain_reward_predictor to just pretrain the reward predictor (200 epochs). Specify the run directory to load initial preferences from with --load_prefs_dir.
  • Load a pretrained reward predictor using the --load_reward_predictor_ckpt argument when running in train_policy_with_preferences mode.

For example, to gather synthetic preferences for MovingDotNoFrameskip-v0, saving to run directory moving_dot-initial_prefs:

$ python gather_initial_prefs MovingDotNoFrameskip-v0 --synthetic_prefs --run_name moving_dot-initial_prefs

Running on FloydHub

To run on FloydHub (a cloud platform for running machine learning jobs), use something like:

floyd run --follow --env tensorflow-1.5 --tensorboard 'bash floydhub_utils/ python --log_dir /output --synthetic_prefs train_policy_with_preferences PongNoFrameskip-v4'

Check out runs reproducing the above results at

Running checkpoints

To run a trained policy checkpoint so you can see what the agent was doing, use Basic usage is:

$ python3 <environment> <policy checkpoint directory>

For example, to run an agent saved in runs/pong:

$ python3 PongNoFrameskip-v4 runs/pong/policy_checkpoints

Architecture notes

There are three main components:

Data flow

The flow of data begins with the A2C workers, which generate video clips of the agent trying things in the environment.

These video clips (referred to in the code as 'segments') are sent to the preference interface. The preference interface shows pairs of video clips to the user and asks through a command-line interface which clip of each pair shows more of the kind of behaviour the user wants.

Preferences are sent to the reward predictor, which trains a deep neural network to predict the each preference from the associated pair of video clips. Preferences are predicted based on a comparison between two penultimate scalar values in the network (one for each video clip) representing some measure of how much the user likes each of the two clips in the pair.

That network can then be used to predict rewards for future video clips by feeding the clip in, running a forward pass to calculate the "how much the user likes this clip" value, then normalising the result to have zero mean and constant variance across time.

This normalised value is then used directly as a reward signal to train the A2C workers according to the preferences given by the user.


All components run asynchronously in different subprocesses:

  • A2C workers explore the environment and train the policy.
  • The preference interface queries the user for preference.
  • The reward predictor is trained using preferences given.

There are three tricky parts to this:

  • Video clips must be sent from the A2C process to the process asking for preferences using a queue. Video clips are cheap, and the A2C process should never stop, so the A2C process only puts a clip onto the queue if the queue is empty, and otherwise drops the clips. The preference interface then just gets as many clips as it can from the queue in 0.5 seconds, in between asking about each pair of clips. (Pairs to show the user are selected from the clip database internal to the preference interface into which clips from the queue are stored.)
  • Preferences must be sent from the preference interface to the reward predictor using a queue. Preferences should never be dropped, though, so the preference interface blocks until the preference can be added to the queue, and the reward predictor training process runs a background thread which constantly receives from the queue, storing preference in the reward predictor process's internal database.
  • Both the A2C process and the reward predictor training process need to access the reward predictor network. This is done using Distributed TensorFlow: each process maintains its own copy of the network, and parameter updates from the reward predictor training process are automatically replicated to the A2C worker process's network.

All subprocesses are started and coordinated by

Changes to the paper's setup

It turned out to be possible to reach the milestones in the results section above even without implementing a number of features described in the original paper.

  • For regularisation of the reward predictor network, the paper uses dropout, batchnorm and an adaptive L2 regularisation scheme. Here, we only use dropout. (Batchnorm is also supported. L2 regularisation is not implemented.)
  • In the paper's setup, the rate at which preferences are requested is gradually reduced over time. We just ask for preferences at a constant rate.
  • The paper selects video clips to show the user based on predicted reward uncertainty among an ensemble of reward predictors. Early experiments suggested a higher chance of successful training by just selecting video clips randomly (also noted by the paper in some situations), so we don't do any ensembling. (Ensembling code is implemented in, but we always operate with only a single-member ensemble, and just chooses segments randomly.)
  • The preference for each pair of video clips is calculated based on a softmax over the predicted latent reward values for each clip. In the paper, "Rather than applying a softmax directly...we assume there is a 10% chance that the human responds uniformly at random. Conceptually this adjustment is needed because human raters have a constant probability of making an error, which doesn’t decay to 0 as the difference in reward difference becomes extreme." I wasn't sure how to implement this - at least, I couldn't see a way to implement it that would actually affect the gradients - so we just do the softmax directly. (Update: see #8.)

Ideas for future work

If you want to hack on this project to learn some deep RL, here are some ideas for extensions and things to investigate:

  • Better ways of selecting video clips for query. As mentioned above and in the paper, it looks like using variance across ensemble members to select video clips to ask the user about sometimes harms performance. Why is this? Is there some inherent reason that "Ask the user about the clips we're most uncertain about" is a bad heuristic (e.g. because then we focus too much on strange examples, and don't sample enough preferences for more common situations)? Or is it a problem with the uncertainty calculation? Do we get different results using dropout-based uncertainty, or by ensembling but with shared parameters?
  • Domain randomisation for the reward predictor. The paper notes that when training an agent to stay alongside other cars in Enduro, "the agent learns to stay almost exactly even with other moving cars for a substantial fraction of the episode, although it gets confused by changes in background". Could this be mitigated with domain randomization? E.g. would randomly changing the colours of the frames encourage the reward predictor to be more invariant to changes in background?
  • Alternative reward predictor architectures. When training Enduro, the user ends up giving enough preferences to cover pretty much the full range of possible car positions on the track. It's therefore unclear how much success in the kinds of simple environments we're playing with here is down to the interesting generalisation capabilities of deep neural networks, and how much it's just memorisation of examples. It could be interesting to explore much simpler architectures of reward predictor - for example, one which tries to establish a ranking of video clips directly from preferences (I'm not familiar with the literature, but e.g. Efficient Ranking from Pairwise Comparisons), then gives reward corresponding to the rank of the most similar video clip.
  • Automatic reward shaping. Watching the graph of rewards predicted by the reward predictor (run with a reward predictor checkpoint), it looks like the predicted rewards might be slightly better-shaped than the original rewards, even when trained with synthetic preferences based on the original rewards. Specifically, in Pong, it looks like there might be a small positive reward whenever the agent hits the ball. Could a reward predictor trained from synthetic preferences be used to automatically shape rewards for easier training?

Code credits

A2C code in a2c is based on the implementation from OpenAI's baselines, commit f8663ea.


Reproduction of OpenAI and DeepMind's "Deep Reinforcement Learning from Human Preferences"







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