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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 enviroment 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. 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, you would define your reward_signals as follows:
reward_signals:
extrinsic:
strength: 1.0
gamma: 0.99
curiosity:
strength: 0.01
gamma: 0.99
encoding_size: 128
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
The 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
The 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 obersvation 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 obseravation 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
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
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
The 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 demonstration, not directly copying the behavior of the demonstrations. It is also especially effective when combined with an Extrinsic signal, but can also be used independently to purely learn from demonstration.
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-ImitationLearning.md).
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
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
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) Samples Per Update
samples_per_update is the maximum number of samples to use during each discriminator update. You may
want to lower this if your buffer size is very large to avoid overfitting the discriminator on current data.
If set to 0, we will use the minimum of buffer size and the number of demonstration samples.
Default Value: 0
Typical Range: Approximately equal to buffer_size
(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