DDPG¶

genrl.agents.deep.ddpg.ddpg module¶

class genrl.agents.deep.ddpg.ddpg.DDPG(*args, noise: genrl.core.noise.ActionNoise = None, noise_std: float = 0.2, **kwargs)[source]¶

Bases: genrl.agents.deep.base.offpolicy.OffPolicyAgentAC

Deep Deterministic Policy Gradient Algorithm

Paper: https://arxiv.org/abs/1509.02971

network¶

The network type of the Q-value function. Supported types: [“cnn”, “mlp”]

Type:	str

env¶

The environment that the agent is supposed to act on

Type:	Environment

create_model¶

Whether the model of the algo should be created when initialised

Type:	bool

batch_size¶

Mini batch size for loading experiences

Type:	int

gamma¶

The discount factor for rewards

Type:	float

layers¶

Layers in the Neural Network of the Q-value function

Type:	`tuple` of `int`

lr_policy¶

Learning rate for the policy/actor

Type:	float

lr_value¶

Learning rate for the critic

Type:	float

replay_size¶

Capacity of the Replay Buffer

Type:	int

buffer_type¶

Choose the type of Buffer: [“push”, “prioritized”]

Type:	str

polyak¶

Target model update parameter (1 for hard update)

Type:	float

noise¶

Action Noise function added to aid in exploration

Type:	`ActionNoise`

noise_std¶

Standard deviation of the action noise distribution

Type:	float

seed¶

Seed for randomness

Type:	int

render¶

Should the env be rendered during training?

Type:	bool

device¶

Hardware being used for training. Options: [“cuda” -> GPU, “cpu” -> CPU]

Type:	str

empty_logs()[source]¶: Empties logs

get_hyperparams() → Dict[str, Any][source]¶

Get relevant hyperparameters to save

Returns:	Hyperparameters to be saved
Return type:	hyperparams (`dict`)

get_logging_params() → Dict[str, Any][source]¶

Gets relevant parameters for logging

Returns:	Logging parameters for monitoring training
Return type:	logs (`dict`)

update_params(update_interval: int) → None[source]¶

Update parameters of the model

Parameters:	update_interval (int) – Interval between successive updates of the target model