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RL Lab · RL-003 · complete
Deep RL Lunar Lander
A neural reinforcement-learning agent can learn a useful landing policy from environment feedback, marking the transition from tabular value tables to function-approximation-based control in the lab.
Experiment setup
Environment and method.
Environment: Lunar Lander
Algorithm: Deep Reinforcement Learning
Status: complete
Outcome
Recorded result.
Built a Deep RL Lunar Lander agent and recorded an agent replay as qualitative evidence of the learned behavior.
What this means
Interpretation and boundary.
This establishes the first completed Deep RL environment in the public learning log. The replay demonstrates learned behavior; standardized reward benchmarks and multi-seed evaluation are intentionally not claimed until they are logged.
Metrics
Recorded metrics.
- Environment: LunarLander
- Method: Deep RL
- Course stage: HF Unit 1
- Replay: Recorded
- Benchmark: Not recorded
Reproducibility
Recorded configuration.
- course unit: Unit 1
- environment: LunarLander
- algorithm: Deep RL
- benchmark: Not recorded
Source and artifacts