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](Training ML-Agents.md) 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 in ML-Agents 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 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

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

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 with training.

Value Estimate

These values should increase with the reward. They corresponds 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 should decrease when reward becomes stable.