Reiforcement Learning package description.
Project description
Gradient-MC-1000-State-Random-Walk
Structure
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Agent
The Agent represents the logic for reinforcement learning. It contains functions for policy evaluation & calculating a State's value.
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Policy
The Policy object determines the actions taken by the agent. The Policy object must inherit from the Base Policy class.
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Environment
The Environment represents the State that the Agent is in. It describes which states are accessible following actions & what their probabilities are.
The Environment depends on the State Space.
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State Space
The State Space describes the states that the agent can be in. Each State is of type State.
The State Space depends on the State.
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State
The State represents a singular State that the agent can be in. The State contains the information that describes each State.
Project details
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