Reinforcement Learning Agent Components for Energy Systems

Agent components for a reinforcement learning model, including state space, action space, and reward function definitions. The dataset was authored by Di Wu and uploaded to figshare in March 2026. Its scope is limited, with a file size of 5.5 KB.

Use Cases

• Define the agent's state space for modeling energy system observations.
• Specify the agent's action space to control energy resource allocation.
• Implement the reward function to optimize for goals like reducing carbon emissions.
• Benchmark RL algorithms using the provided formal agent structure.

Strengths

• Clearly defined core RL agent components (state, action, reward).
• Released under a permissive CC-BY-4.0 license.
• Uploaded in March 2026, indicating recent availability.

Limitations

• Extremely small scale at 5.5 KB, suggesting a conceptual framework rather than operational data.
• No sample data or row/column counts provided to assess completeness.
• Platform tags are promotional and may not accurately describe the data's technical content.

Provenance

Source

figshare, author Di Wu.

Collection Method

null

Time Range

null

Freshness

Last updated March 2026.

Geography

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