Agents¶

Here you will find all your running agents and be able to create a new one. When entering you will see a list of agents that you have previously created with some quick overview.

A bit more specifically, you’ll see information like:

Name of the agent
Type of the agent, e.g. DQN, PPO or C51
Is Active which indicates whether agent can be accessed
Description of the agent which you gave
Configuration of the agent

Your agents are shown in a table which has a limit of view. By default that’s five agents per page. To navigate between pages use arrows in the bottom right corner, or use the search option just above the table.

Create agent¶

Manual creation¶

When you click “Create Agent” it will take you to the agent creation page. To create an agent the following are expected:

Agent Create options¶
Option	Description
Name	Name of your agent. It has to be unique per user.
Model	Type of the agent, for example DQN or DDPG.
Description	Text indicating something specific about this agent.
Is Active	Checkbox whether to create an active agent.
State size	Number of values to expect from observations.
Action size	For discrete problem, that’s a number of potential values. For continuous, that’ number of values.
Agent Configuration	JSON configuration of the agent.

Note

We understand that providing a JSON into configuration might not be the best user interface and so we will promise an improvement. For now, however, please see Agent Configuration for details on each agent. Feel free to reach out and let us know about your preferences. We are prioritizing work based on demand.

From a Snapshot¶

Another option to create an agent is through uploading a Snapshot. This is a preferred method when you have already a Snapshot of a trained agent. Snapshots can be obtained be either sharing them with other users, or downloading a snapshot from one of your agents.

To create an agent from a Snapshot go to Agents and click on the From Snapshot button. This will take you to a form where you’ll be able to type new agent’s name and select where your snapshot file is located. Once both options are properly filled the Save button should be available. After submitting the form you should see the agent on the Agents view page.

Agent Configuration¶

Although most agents are created the same way, their configuration will have a slightly different impact. Below are all available options to pass to each agent, their default values and their descriptions.

DQN¶

All values relate to the ones in the AI Traineree DQN.

DQN agent configuration¶
Key	Default	Type	Description
hidden_layers	(64, 64)	tuple of ints	Tuple defining hidden dimensions in fully connected nets.
lr	3e-(4	float	Learning rate
gamma	0.99	float	discount factor
tau	0.002	float	soft-copy factor
update_freq	1	int	Number of steps between each learning.
batch_size	64	int	Number of samples to use in learning.
buffer_size	1e5	int	Size of the experience buffer.
warm_up	0	int	How many samples to wait before performing learning.
number_updates	1	int	Per learning, how many times to compute error and update agent.
max_grad_norm	10	float	Maximum at which the gradient is capped.
using_double_q	true	bool	Whether to use double Q value
n_steps	1	int	N steps reward lookahead

PPO¶

All values relate to the ones in the AI Traineree PPO.

PPO agent configuration¶
Key	Default	Type	Description
hidden_layers	(100, 100)	tuple of ints	Tuple defining hidden dimensions in fully connected nets.
is_discrete	False	bool	Whether return discrete action.
kl_div	False	bool	Whether to use KL divergence in loss.
using_gae	True	bool	Whether to use General Advantage Estimator.
gae_lambda	0.96	float	Value of lambda in GAE.
actor_lr	0.0003	float	Learning rate for the actor (policy).
critic_lr	0.001	float	Learning rate for the critic (value function).
actor_betas	(0.9, 0.999)	tuple of floats	Adam’s betas for actor optimizer.
critic_betas	(0.9, 0.999)	tulple of floats	Adam’s betas for critic optimizer.
gamma	0.99	float	Discount value.
ppo_ratio_clip	0.25	float	Policy ratio clipping value.
num_epochs	1	int	Number of time to learn from samples.
rollout_length	48	int	Number of actions to take before update.
batch_size	rollout_length	int	Number of samples used in learning.
actor_number_updates	10	int	Number of times policy losses are propagated.
critic_number_updates	10	int	Number of times value losses are propagated.
entropy_weight	0.005	float	Weight of the entropy term in the loss.
value_loss_weight	0.005	float	Weight of the entropy term in the loss.

DDPG¶

All values relate to the ones in the AI Traineree DDPG.

DDPG agent configuration¶
Key	Default	Type	Description
hidden_layers	(128, 128)	tuple of ints	Tuple defining hidden dimensions in fully connected nets.
actor_lr	3e-4	float	Actor specific learning rate
critic_lr	3e-4	float	Critic specific learning rate
gamma	0.99	float	discount factor
tau	0.002	float	soft-copy factor
update_freq	1	int	Number of steps between each learning.
batch_size	64	int	Number of samples to use in learning.
buffer_size	1e5	int	Size of the experience buffer.
warm_up	0	int	How many samples to wait before performing learning.
number_updates	1	int	Per learning, how many times to compute error and update agent.
max_grad_norm_actor	10	float	Maximum at which critic’s gradient is capped.
max_grad_norm_critic	10	float	Maximum at which actor’s gradient is capped.
action_min	-1	float	Minimum returned action value
action_max	1	float	Maximum returned action value
action_scale	1	float	How much to scale action value (std var in action distribution)

Rainbow¶

All values relate to the ones in the AI Traineree Rainbow.

Rainbow agent configuration¶
Key	Default	Type	Description
hidden_layers	(100, 100)	tuple of ints	Shape and sizes of fully connected networks used.
lr	1e-3	float	Learning rate value.
gamma	0.99	float	Discount factor.
tau	0.002	float	Soft-copy factor.
update_freq	1	int	Number of steps between each learning step.
batch_size	80	int	Number of samples to use at each learning step.
buffer_size	1e5	int	Number of most recent samples to keep in memory for learning.
warm_up	0	int	Number of samples to observe before starting any learning step.
number_updates	1	int	How many times to use learning step in the learning phase.
max_grad_norm	10	float	Maximum norm of the gradient used in learning.
using_double_q	True	bool	Whether to use Double Q Learning network.
n_steps	3	int	Number of lookahead steps when estimating reward.
v_min	-10	float	Lower bound for distributional value V.
v_max	10	float	Upper bound for distributional value V.
num_atoms	21	int	Number of atoms (discrete states) in the value V distribution.