Agents:

• Agent argument update_frequency / update[frequency] now supports float values > 0.0, which specify the update-frequency relative to the batch-size
• Changed default value for argument update_frequency from 1.0 to 0.25 for DQN, DoubleDQN, DuelingDQN agents
• New argument return_processing and advantage_processing (where applicable) for all agent sub-types
• New function Agent.get_specification() which returns the agent specification as dictionary
• New function Agent.get_architecture() which returns a string representation of the network layer architecture

Modules:

• Improved and simplified module specification, for instance: network=my_module instead of network=my_module.TestNetwork, or environment=envs.custom_env instead of environment=envs.custom_env.CustomEnvironment (module file needs to be in the same directory or a sub-directory)

Networks:

• New argument single_output=True for some policy types which, if False, allows the specification of additional network outputs for some/all actions via registered tensors
• KerasNetwork argument model now supports arbitrary functions as long as they return a tf.keras.Model

Layers:

• New layer type SelfAttention (specification key: self_attention)

Parameters:

• Support tracking of non-constant parameter values

Runner:

• Rename attribute episode_rewards as episode_returns, and TQDM status reward as return
• Extend argument agent to support Agent.load() keyword arguments to load an existing agent instead of creating a new one.

Examples:

• Added action_masking.py example script to illustrate an environment implementation with built-in action masking.

Buxfixes:

• Customized device placement was not applied to most tensors