# Training with Proximal Policy Optimization This section is still to be written. Refer to [Getting Started with the Balance Ball Environment](Getting-Started-with-Balance-Ball.md) for a walk-through of the PPO training process. ## Best Practices when training with PPO The process of training a Reinforcement Learning model can often involve the need to tune the hyperparameters in order to achieve a level of performance that is desirable. 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](./Getting-Started-with-Balance-Ball.md#observing-training-progress). #### 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.