ml-agents/docs/Training-SAC.md

Training with Soft-Actor Critic

In addition to Proximal Policy Optimization (PPO), ML-Agents also provides Soft Actor-Critic to perform reinforcement learning.

In contrast with PPO, SAC is off-policy, which means it can learn from experiences collected at any time during the past. As experiences are collected, they are placed in an experience replay buffer and randomly drawn during training. This makes SAC significantly more sample-efficient, often requiring 5-10 times less samples to learn the same task as PPO. However, SAC tends to require more model updates. SAC is a good choice for heavier or slower environments (about 0.1 seconds per step or more).

SAC is also a "maximum entropy" algorithm, and enables exploration in an intrinsic way. Read more about maximum entropy RL here.

To train an agent, you will need to provide the agent one or more reward signals which the agent should attempt to maximize. See Reward Signals for the available reward signals and the corresponding hyperparameters.

Best Practices when training with SAC

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

Hyperparameters

Reward Signals

In reinforcement learning, the goal is to learn a Policy that maximizes reward. In the most basic case, the reward is given by the environment. However, we could imagine rewarding the agent for various different behaviors. For instance, we could reward the agent for exploring new states, rather than explicitly defined reward signals. Furthermore, we could mix reward signals to help the learning process.

reward_signals provides a section to define reward signals. ML-Agents provides two reward signals by default, the Extrinsic (environment) reward, and the Curiosity reward, which can be used to encourage exploration in sparse extrinsic reward environments.

Number of Updates for Reward Signal (Optional)

reward_signal_num_update for the reward signals corresponds to the number of mini batches sampled and used for updating the reward signals during each update. By default, we update the reward signals once every time the main policy is updated. However, to imitate the training procedure in certain imitation learning papers (e.g. Kostrikov et. al, Blondé et. al), we may want to update the policy N times, then update the reward signal (GAIL) M times. We can change train_interval and num_update of SAC to N, as well as reward_signal_num_update under reward_signals to M to accomplish this. By default, reward_signal_num_update is set to num_update.

Typical Range: num_update

Buffer Size

buffer_size corresponds the maximum number of experiences (agent observations, actions and rewards obtained) that can be stored in the experience replay buffer. This value should be large, on the order of thousands of times longer than your episodes, so that SAC can learn from old as well as new experiences. It should also be much larger than batch_size.

Typical Range: 50000 - 1000000

Buffer Init Steps

buffer_init_steps is the number of experiences to prefill the buffer with before attempting training. As the untrained policy is fairly random, prefilling the buffer with random actions is useful for exploration. Typically, at least several episodes of experiences should be prefilled.

Typical Range: 1000 - 10000

Batch Size

batch_size is the number of experiences used for one iteration of a gradient descent update. 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): 128 - 1024

Typical Range (Discrete): 32 - 512

Initial Entropy Coefficient

init_entcoef refers to the initial entropy coefficient set at the beginning of training. In SAC, the agent is incentivized to make its actions entropic to facilitate better exploration. The entropy coefficient weighs the true reward with a bonus entropy reward. The entropy coefficient is automatically adjusted to a preset target entropy, so the init_entcoef only corresponds to the starting value of the entropy bonus. Increase init_entcoef to explore more in the beginning, decrease to converge to a solution faster.

Typical Range (Continuous): 0.5 - 1.0

Typical Range (Discrete): 0.05 - 0.5

Train Interval

train_interval is the number of steps taken between each agent training event. Typically, we can train after every step, but if your environment's steps are very small and very frequent, there may not be any new interesting information between steps, and train_interval can be increased.

Typical Range: 1 - 5

Number of Updates

num_update corresponds to the number of mini batches sampled and used for training during each training event. In SAC, a single "update" corresponds to grabbing a batch of size batch_size from the experience replay buffer, and using this mini batch to update the models. Typically, this can be left at 1. However, to imitate the training procedure in certain papers (e.g. Kostrikov et. al, Blondé et. al), we may want to update N times with different mini batches before grabbing additional samples. We can change train_interval and num_update to N to accomplish this.

Typical Range: 1

Tau

tau corresponds to the magnitude of the target Q update during the SAC model update. In SAC, there are two neural networks: the target and the policy. The target network is used to bootstrap the policy's estimate of the future rewards at a given state, and is fixed while the policy is being updated. This target is then slowly updated according to tau. Typically, this value should be left at 0.005. For simple problems, increasing tau to 0.01 might reduce the time it takes to learn, at the cost of stability.

Typical Range: 0.005 - 0.01

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

(Optional) Learning Rate Schedule

learning_rate_schedule corresponds to how the learning rate is changed over time. For SAC, we recommend holding learning rate constant so that the agent can continue to learn until its Q function converges naturally.

Options:

• linear: Decay learning_rate linearly, reaching 0 at max_steps.
• constant (default): Keep learning rate constant for the entire training run.

Options: linear, constant

Time Horizon

time_horizon corresponds to how many steps of experience to collect per-agent before adding it to the experience buffer. This parameter is a lot less critical to SAC than PPO, and can typically be set to approximately your episode length.

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

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) Visual Encoder Type

vis_encode_type corresponds to the encoder type for encoding visual observations. Valid options include:

• simple (default): a simple encoder which consists of two convolutional layers
• nature_cnn: CNN implementation proposed by Mnih et al.(https://www.nature.com/articles/nature14236), consisting of three convolutional layers
• resnet: IMPALA Resnet implementation (https://arxiv.org/abs/1802.01561), consisting of three stacked layers, each with two risidual blocks, making a much larger network than the other two.

Options: simple, nature_cnn, resnet

(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) Save Replay Buffer

save_replay_buffer enables you to save and load the experience replay buffer as well as the model when quitting and re-starting training. This may help resumes go more smoothly, as the experiences collected won't be wiped. Note that replay buffers can be very large, and will take up a considerable amount of disk space. For that reason, we disable this feature by default.

Default: False

(Optional) Pretraining Using Demonstrations

In some cases, you might want to bootstrap the agent's policy using behavior recorded from a player. This can help guide the agent towards the reward. Pretraining adds training operations that mimic a demonstration rather than attempting to maximize reward. It is essentially equivalent to running behavioral cloning in-line with SAC.

To use pretraining, add a pretraining section to the trainer_config. For instance:

    pretraining:
        demo_path: ./demos/ExpertPyramid.demo
        strength: 0.5
        steps: 10000

Below are the available hyperparameters for pretraining.

Strength

strength corresponds to the learning rate of the imitation relative to the learning rate of SAC, and roughly corresponds to how strongly we allow the behavioral cloning to influence the policy.

Typical Range: 0.1 - 0.5

Demo Path

demo_path is the path to your .demo file or directory of .demo files. See the imitation learning guide for more on .demo files.

Steps

During pretraining, it is often desirable to stop using demonstrations after the agent has "seen" rewards, and allow it to optimize past the available demonstrations and/or generalize outside of the provided demonstrations. steps corresponds to the training steps over which pretraining is active. The learning rate of the pretrainer will anneal over the steps. Set the steps to 0 for constant imitation over the entire training run.

(Optional) Batch Size

batch_size is the number of demonstration experiences used for one iteration of a gradient descent update. If not specified, it will default to the batch_size defined for SAC.

Typical Range (Continuous): 512 - 5120

Typical Range (Discrete): 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 Coefficient

SAC is a "maximum entropy" reinforcement learning algorithm, and agents trained using SAC are incentivized to behave randomly while also solving the problem. The entropy coefficient balances the incentive to behave randomly vs. maximizing the reward. This value is adjusted automatically so that the agent retains some amount of randomness during training. It should steadily decrease in the beginning of training, and reach some small value where it will level off. If it decreases too soon or takes too long to decrease, init_entcoef should be adjusted.

Entropy

This corresponds to how random the decisions of a Brain are. This should initially increase during training, reach a peak, and should decline along with the Entropy Coefficient. This is because in the beginning, the agent is incentivized to be more random for exploration due to a high entropy coefficient. If it decreases too soon or takes too long to decrease, init_entcoef should be adjusted.

Learning Rate

This will stay a constant value by default, unless learning_rate_schedule is set to linear.

Policy Loss

These values may increase as the agent explores, but should decrease long-term as the agent learns how to solve the task.

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. They may also increase at the beginning as the agent is rewarded for being random (see: Entropy and Entropy Coefficient), but should decline as Entropy Coefficient decreases.

Value Loss

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