ml-agents/docs/Training-PPO.md

Training with Proximal Policy Optimization

ML-Agents uses a reinforcement learning technique called Proximal Policy Optimization (PPO). PPO uses a neural network to approximate the ideal function that maps an agent's observations to the best action an agent can take in a given state. The ML-Agents PPO algorithm is implemented in TensorFlow and runs in a separate Python process (communicating with the running Unity application over a socket).

See Training ML-Agents for instructions on running the training program, learn.py.

If you are using the recurrent neural network (RNN) to utilize memory, see Using Recurrent Neural Networks for RNN-specific training details.

If you are using curriculum training to pace the difficulty of the learning task presented to an agent, see Training with Curriculum Learning.

For information about imitation learning, which uses a different training algorithm, see Training with Imitation Learning.

Best Practices when training with PPO

Successfully training a Reinforcement Learning model often involves tuning the training hyperparameters. This guide contains some best practices for tuning the training process when the default parameters don't seem to be giving the level of performance you would like.

Hyperparameters

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

Lambda

lambd corresponds to the lambda parameter used when calculating the Generalized Advantage Estimate (GAE). This can be thought of as how much the agent relies on its current value estimate when calculating an updated value estimate. Low values correspond to relying more on the current value estimate (which can be high bias), and high values correspond to relying more on the actual rewards received in the environment (which can be high variance). The parameter provides a trade-off between the two, and the right value can lead to a more stable training process.

Typical Range: 0.9 - 0.95

Buffer Size

buffer_size corresponds to how many experiences (agent observations, actions and rewards obtained) should be collected before we do any learning or updating of the model. This should be a multiple of batch_size. Typically larger buffer_size correspond to more stable training updates.

Typical Range: 2048 - 409600

Batch Size

batch_size is the number of experiences used for one iteration of a gradient descent update. This should always be a fraction of the buffer_size. If you are using a continuous action space, this value should be large (in the order of 1000s). If you are using a discrete action space, this value should be smaller (in order of 10s).

Typical Range (Continuous): 512 - 5120

Typical Range (Discrete): 32 - 512

Number of Epochs

num_epoch is the number of passes through the experience buffer during gradient descent. The larger the batch_size, the larger it is acceptable to make this. Decreasing this will ensure more stable updates, at the cost of slower learning.

Typical Range: 3 - 10

Learning Rate

learning_rate corresponds to the strength of each gradient descent update step. This should typically be decreased if training is unstable, and the reward does not consistently increase.

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

Time Horizon

time_horizon corresponds to how many steps of experience to collect per-agent before adding it to the experience buffer. When this limit is reached before the end of an episode, a value estimate is used to predict the overall expected reward from the agent's current state. As such, this parameter trades off between a less biased, but higher variance estimate (long time horizon) and more biased, but less varied estimate (short time horizon). In cases where there are frequent rewards within an episode, or episodes are prohibitively large, a smaller number can be more ideal. This number should be large enough to capture all the important behavior within a sequence of an agent's actions.

Typical Range: 32 - 2048

Max Steps

max_steps corresponds to how many steps of the simulation (multiplied by frame-skip) are run during the training process. This value should be increased for more complex problems.

Typical Range: 5e5 - 1e7

Beta

beta corresponds to the strength of the entropy regularization, which makes the policy "more random." This ensures that agents properly explore the action space during training. Increasing this will ensure more random actions are taken. This should be adjusted such that the entropy (measurable from TensorBoard) slowly decreases alongside increases in reward. If entropy drops too quickly, increase beta. If entropy drops too slowly, decrease beta.

Typical Range: 1e-4 - 1e-2

Epsilon

epsilon corresponds to the acceptable threshold of divergence between the old and new policies during gradient descent updating. Setting this value small will result in more stable updates, but will also slow the training process.

Typical Range: 0.1 - 0.3

Normalize

normalize corresponds to whether normalization is applied to the vector observation inputs. This normalization is based on the running average and variance of the vector observation. Normalization can be helpful in cases with complex continuous control problems, but may be harmful with simpler discrete control problems.

Number of Layers

num_layers corresponds to how many hidden layers are present after the observation input, or after the CNN encoding of the visual observation. For simple problems, fewer layers are likely to train faster and more efficiently. More layers may be necessary for more complex control problems.

Typical range: 1 - 3

Hidden Units

hidden_units correspond to how many units are in each fully connected layer of the neural network. For simple problems where the correct action is a straightforward combination of the observation inputs, this should be small. For problems where the action is a very complex interaction between the observation variables, this should be larger.

Typical Range: 32 - 512

(Optional) Recurrent Neural Network Hyperparameters

The below hyperparameters are only used when use_recurrent is set to true.

Sequence Length

sequence_length corresponds to the length of the sequences of experience passed through the network during training. This should be long enough to capture whatever information your agent might need to remember over time. For example, if your agent needs to remember the velocity of objects, then this can be a small value. If your agent needs to remember a piece of information given only once at the beginning of an episode, then this should be a larger value.

Typical Range: 4 - 128

Memory Size

memory_size corresponds to the size of the array of floating point numbers used to store the hidden state of the recurrent neural network. This value must be a multiple of 4, and should scale with the amount of information you expect the agent will need to remember in order to successfully complete the task.

Typical Range: 64 - 512

(Optional) Intrinsic Curiosity Module Hyperparameters

The below hyperparameters are only used when use_curiosity is set to true.

Curioisty Encoding Size

curiosity_enc_size corresponds to the size of the hidden layer used to encode the observations within the intrinsic curiosity module. This value should be small enough to encourage the curiosity module to compress the original observation, but also not too small to prevent it from learning the dynamics of the environment.

Typical Range: 64 - 256

Curiosity Strength

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

Typical Range: 0.1 - 0.001

Training Statistics

To view training statistics, use TensorBoard. For information on launching and using TensorBoard, see here.

Cumulative Reward

The general trend in reward should consistently increase over time. Small ups and downs are to be expected. Depending on the complexity of the task, a significant increase in reward may not present itself until millions of steps into the training process.

Entropy

This corresponds to how random the decisions of a brain are. This should consistently decrease during training. If it decreases too soon or not at all, beta should be adjusted (when using discrete action space).

Learning Rate

This will decrease over time on a linear schedule.

Policy Loss

These values will oscillate during training. Generally they should be less than 1.0.

Value Estimate

These values should increase as the cumulative reward increases. They correspond to how much future reward the agent predicts itself receiving at any given point.

Value Loss

These values will increase as the reward increases, and then should decrease once reward becomes stable.