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Doc fixes

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Marek Wolan
2024-03-15 09:22:55 +00:00
parent d33c80d0d6
commit a9bf0981e6
2 changed files with 3 additions and 5 deletions
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4
docs/source/game_layer.rst
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@@ -26,10 +26,8 @@ Agents
All agents inherit from the :py:class:`primaite.game.agent.interface.AbstractAgent` class, which mandates that they have an ObservationManager, ActionManager, and RewardManager. The agent behaviour depends on the type of agent, but there are two main types:
* RL agents action during each step is decided by an appropriate RL algorithm. The agent within PrimAITE just acts to format and forward actions decided by an RL policy.
* Deterministic agents perform all of their decision making within the PrimAITE game layer. They typically have a scripted policy which always performs the same action or a rule-based policy which performs actions based on the current state of the simulation. They can have a stochastic element, and their seed will be settable.
* Deterministic agents perform all of their decision making within the PrimAITE game layer. They typically have a scripted policy which always performs the same action or a rule-based policy which performs actions based on the current state of the simulation. They can have a stochastic element, and their seed is settable.
..
TODO: add seed to stochastic scripted agents
Observations
============

4
src/primaite/game/agent/rewards.py
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@@ -319,11 +319,11 @@ class SharedReward(AbstractReward):
"""
Initialise the shared reward.
The agent_ref is a placeholder value. It starts off as none, but it must be set before this reward can work
The agent_name is a placeholder value. It starts off as none, but it must be set before this reward can work
correctly.
:param agent_name: The name whose reward is an input
:type agent_ref: Optional[str]
:type agent_name: Optional[str]
"""
self.agent_name = agent_name
"""Agent whose reward to track."""