ml-agents/docs/Reward-Signals.md

Reward Signals

In reinforcement learning, the end goal for the Agent is to discover a behavior (a Policy) that maximizes a reward. Typically, a reward is defined by your environment, and corresponds to reaching some goal. These are what we refer to as "extrinsic" rewards, as they are defined external of the learning algorithm.

Rewards, however, can be defined outside of the environment as well, to encourage the agent to behave in certain ways, or to aid the learning of the true extrinsic reward. We refer to these rewards as "intrinsic" reward signals. The total reward that the agent will learn to maximize can be a mix of extrinsic and intrinsic reward signals.

ML-Agents allows reward signals to be defined in a modular way, and we provide three reward signals that can the mixed and matched to help shape your agent's behavior. The extrinsic Reward Signal represents the rewards defined in your environment, and is enabled by default. The curiosity reward signal helps your agent explore when extrinsic rewards are sparse.

Enabling Reward Signals

Reward signals, like other hyperparameters, are defined in the trainer config .yaml file. An example is provided in config/trainer_config.yaml and config/gail_config.yaml. To enable a reward signal, add it to the reward_signals: section under the brain name. For instance, to enable the extrinsic signal in addition to a small curiosity reward and a GAIL reward signal, you would define your reward_signals as follows:

reward_signals:
    extrinsic:
        strength: 1.0
        gamma: 0.99
    curiosity:
        strength: 0.02
        gamma: 0.99
        encoding_size: 256
    gail:
        strength: 0.01
        gamma: 0.99
        encoding_size: 128
        demo_path: demos/ExpertPyramid.demo

Each reward signal should define at least two parameters, strength and gamma, in addition to any class-specific hyperparameters. Note that to remove a reward signal, you should delete its entry entirely from reward_signals. At least one reward signal should be left defined at all times.

Reward Signal Types

As part of the toolkit, we provide three reward signal types as part of hyperparameters - Extrinsic, Curiosity, and GAIL.

Extrinsic Reward Signal

The extrinsic reward signal is simply the reward given by the environment. Remove it to force the agent to ignore the environment reward.

Strength

strength is the factor by which to multiply the raw reward. Typical ranges will vary depending on the reward signal.

Typical Range: 1.0

Gamma

gamma corresponds to the discount factor for future rewards. This can be thought of as how far into the future the agent should care about possible rewards. In situations when the agent should be acting in the present in order to prepare for rewards in the distant future, this value should be large. In cases when rewards are more immediate, it can be smaller.

Typical Range: 0.8 - 0.995

Curiosity Reward Signal

The curiosity Reward Signal enables the Intrinsic Curiosity Module. This is an implementation of the approach described in "Curiosity-driven Exploration by Self-supervised Prediction" by Pathak, et al. It trains two networks:

• an inverse model, which takes the current and next observation of the agent, encodes them, and uses the encoding to predict the action that was taken between the observations
• a forward model, which takes the encoded current observation and action, and predicts the next encoded observation.

The loss of the forward model (the difference between the predicted and actual encoded observations) is used as the intrinsic reward, so the more surprised the model is, the larger the reward will be.

For more information, see

• https://arxiv.org/abs/1705.05363
• https://pathak22.github.io/noreward-rl/
• https://blogs.unity3d.com/2018/06/26/solving-sparse-reward-tasks-with-curiosity/

Strength

In this case, strength corresponds to the magnitude of the curiosity reward generated by the intrinsic curiosity module. This should be scaled in order to ensure it is large enough to not be overwhelmed by extrinsic reward signals in the environment. Likewise it should not be too large to overwhelm the extrinsic reward signal.

Typical Range: 0.001 - 0.1

Gamma

gamma corresponds to the discount factor for future rewards.

Typical Range: 0.8 - 0.995

(Optional) Encoding Size

encoding_size corresponds to the size of the encoding used by the intrinsic curiosity model. This value should be small enough to encourage the ICM to compress the original observation, but also not too small to prevent it from learning to differentiate between demonstrated and actual behavior.

Default Value: 64

Typical Range: 64 - 256

(Optional) Learning Rate

learning_rate is the learning rate used to update the intrinsic curiosity module. This should typically be decreased if training is unstable, and the curiosity loss is unstable.

Default Value: 3e-4

Typical Range: 1e-5 - 1e-3

GAIL Reward Signal

GAIL, or Generative Adversarial Imitation Learning, is an imitation learning algorithm that uses an adversarial approach, in a similar vein to GANs (Generative Adversarial Networks). In this framework, a second neural network, the discriminator, is taught to distinguish whether an observation/action is from a demonstration or produced by the agent. This discriminator can the examine a new observation/action and provide it a reward based on how close it believes this new observation/action is to the provided demonstrations.

At each training step, the agent tries to learn how to maximize this reward. Then, the discriminator is trained to better distinguish between demonstrations and agent state/actions. In this way, while the agent gets better and better at mimicing the demonstrations, the discriminator keeps getting stricter and stricter and the agent must try harder to "fool" it.

This approach, when compared to Behavioral Cloning, requires far fewer demonstrations to be provided. After all, we are still learning a policy that happens to be similar to the demonstrations, not directly copying the behavior of the demonstrations. It is especially effective when combined with an Extrinsic signal. However, the GAIL reward signal can also be used independently to purely learn from demonstrations.

Using GAIL requires recorded demonstrations from your Unity environment. See the imitation learning guide to learn more about recording demonstrations.

Strength

strength is the factor by which to multiply the raw reward. Note that when using GAIL with an Extrinsic Signal, this value should be set lower if your demonstrations are suboptimal (e.g. from a human), so that a trained agent will focus on receiving extrinsic rewards instead of exactly copying the demonstrations. Keep the strength below about 0.1 in those cases.

Typical Range: 0.01 - 1.0

Gamma

gamma corresponds to the discount factor for future rewards.

Typical Range: 0.8 - 0.9

Demo Path

demo_path is the path to your .demo file or directory of .demo files. See the [imitation learning guide] (Training-Imitation-Learning.md).

(Optional) Encoding Size

encoding_size corresponds to the size of the hidden layer used by the discriminator. This value should be small enough to encourage the discriminator to compress the original observation, but also not too small to prevent it from learning to differentiate between demonstrated and actual behavior. Dramatically increasing this size will also negatively affect training times.

Default Value: 64

Typical Range: 64 - 256

(Optional) Learning Rate

learning_rate is the learning rate used to update the discriminator. This should typically be decreased if training is unstable, and the GAIL loss is unstable.

Default Value: 3e-4

Typical Range: 1e-5 - 1e-3

(Optional) Use Actions

use_actions determines whether the discriminator should discriminate based on both observations and actions, or just observations. Set to True if you want the agent to mimic the actions from the demonstrations, and False if you'd rather have the agent visit the same states as in the demonstrations but with possibly different actions. Setting to False is more likely to be stable, especially with imperfect demonstrations, but may learn slower.

Default Value: false

(Optional) Variational Discriminator Bottleneck

use_vail enables a variational bottleneck within the GAIL discriminator. This forces the discriminator to learn a more general representation and reduces its tendency to be "too good" at discriminating, making learning more stable. However, it does increase training time. Enable this if you notice your imitation learning is unstable, or unable to learn the task at hand.

Default Value: false