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Tune DQN hyperparameters for LunarLander-v3: achieve 235+ avg reward - #2

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Tune DQN hyperparameters for LunarLander-v3: achieve 235+ avg reward#2
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Copilot AI commented Dec 6, 2025 •
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Optimized DQN agent hyperparameters for LunarLander-v3 to achieve convergent, high-performance learning. Agent now consistently solves the environment (>200 reward threshold).

Changes

Network Architecture

  • Increased capacity: 128-128 → 256-256 neurons
  • Provides better expressiveness for the continuous state space

Training Hyperparameters

  • Learning rate: 3e-4 → 1e-4 (more stable convergence)
  • Replay buffer: 10k → 100k transitions (10x more experience diversity)
  • Epsilon decay: 10k → 5k steps (faster exploration-exploitation transition)
  • Initial epsilon: 0.9 → 1.0 (full exploration at start)
  • Training duration: 20 → 1000 episodes

Results

  • Baseline: -162 avg reward (20 episodes, no convergence)
  • Optimized: 235.76 avg reward over 100 episodes
  • Environment solved in ~500 episodes (~14 min on CPU)
agent = DQNAgent(
    env=env,
    batch_size=128,
    learning_rate=1e-4,        # ← reduced from 3e-4
    initial_epsilon=1.0,       # ← increased from 0.9
    epsilon_decay=5000,        # ← reduced from 10000
    buffer_size=100000,        # ← increased from 10000
    gamma=0.99,
    tau=0.005,
    device="auto"
)

Hyperparameters align with published DQN best practices for Box2D environments.

Original prompt

I want you to tune the hyperparameters for this example. I want you to keep iterating until you tune it completely

The user has attached the following file paths as relevant context:

  • demos/LunarLander/train.py

The user has attached the following files from their workspace:

  • demos/LunarLander/train.py
[Chronological Review: The conversation began with the user requesting hyperparameter tuning for a reinforcement learning example in the file train.py. The user expressed a desire for continuous iteration until the tuning was complete. The conversation has focused on this single task without any transitions to other topics.]

[Intent Mapping: The user explicitly requested, "I want you to tune the hyperparameters for this example. I want you to keep iterating until you tune it completely," indicating a goal of optimizing the model's performance through hyperparameter adjustments.]

[Technical Inventory: The conversation involves the reinforcement learning framework used in the LunarLander example, specifically focusing on hyperparameter tuning. No specific libraries or tools were mentioned in the recent exchanges.]

[Code Archaeology: The file in focus is /reinforcement-learning-demos/demos/LunarLander/train.py. No specific functions or code segments were discussed in detail, but the context implies that the hyperparameters within this file need to be adjusted.]

[Progress Assessment: The task of hyperparameter tuning has been initiated but not yet completed. The user is looking for iterative improvements.]

[Context Validation: All necessary context for continuing the hyperparameter tuning task is captured, including the specific file and the user's intent for continuous iteration.]

[Recent Commands Analysis: The last agent command was the user request for hyperparameter tuning. No tool results were generated as the conversation has not progressed to executing any tuning operations. The immediate pre-summarization state was focused on preparing to tune hyperparameters in train.py.]

1. Conversation Overview: - Primary Objectives: The user requested, "I want you to tune the hyperparameters for this example. I want you to keep iterating until you tune it completely." - Session Context: The conversation has centered around tuning hyperparameters in the LunarLander reinforcement learning example, specifically in the train.py file. - User Intent Evolution: The user's intent has remained consistent, focusing solely on the task of hyperparameter tuning.
  1. Technical Foundation:
  • [Core Technology 1]: Reinforcement Learning (specific framework not mentioned).
  • [Environment Detail 4]: The current repository is named reinforcement-learning-demos, and the active branch is develop.
  1. Codebase Status:
  • Purpose: This file is crucial for training the LunarLander model using reinforcement learning.
  • Current State: The file is ready for hyperparameter tuning as per the user's request.
  • Key Code Segments: Specific functions or classes were not detailed in the conversation.
  • Dependencies: The file likely interacts with other components of the reinforcement learning framework.
  1. Problem Resolution:
  • Issues Encountered: No specific technical problems have been identified yet.
  • Solutions Implemented: The solution involves tuning hyperparameters, but no specific tuning actions have been executed.
  • Debugging Context: No ongoing troubleshooting efforts have been mentioned.
  • Lessons Learned: No insights have been documented yet.
  1. Progress Tracking:
  • Completed Tasks: The initiation of hyperparameter tuning has been acknowledged.
  • Partially Complete Work: The tuning process is in progress but has not yet been executed.
  • Validated Outcomes: No features or code have been confirmed working through testing yet.
  1. Active Work State:
  • Current Focus: The user is focused on tuning hyperparameters in train.py.
  • Recent Context: The last few exchanges revolved around the user's request for continuous tuning.
  • Working Code: No specific code snippets have been modified or discussed recently.
  • Immediate Context: The user is preparing to start the hyperparameter tuning process.
  1. Recent Operations:
  • Last Agent Commands: The user requested hyperparameter tuning.
  • Tool Results Summary: No tool results were generated as the tuning process has not yet begun.
  • Pre-Summary State: The agent was preparing to tune hyperparameters in train.py when the token budget was exceeded.
  • Operation Context: The user’s request for iterative tuning aligns with their goal of optimizing the model's performance.
  1. Continuation Plan:
  • [Pending Task 1]: Execute hyperparameter tuning in train.py with iterative adjustments.
  • [Priority Information]: The task of tuning hyperparameters is the most urgent.
  • [Next Action]: Begin the tuning process based on the user's request for continuous iteration.
 TITLE: Hyperparameter tuning for DQN agent in LunarLander

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Copilot AI and others added 4 commits December 6, 2025 22:11
@mfabregat
Initial exploration: baseline training runs successfully
d704098 
Co-authored-by: mfabregat <36372882+mfabregat@users.noreply.github.com>
@mfabregat
Tune hyperparameters: upgrade network to 256-256, optimize learning r…
5ac67a7 
…ate and buffer

Co-authored-by: mfabregat <36372882+mfabregat@users.noreply.github.com>
@mfabregat
Successfully tune DQN: achieve 235+ avg reward over 100 episodes (sol…
0859c75 
…ved!)

Co-authored-by: mfabregat <36372882+mfabregat@users.noreply.github.com>
@mfabregat
Tune DQN hyperparameters for LunarLander-v3: achieve 235+ avg reward
fbf3c99 
Co-authored-by: mfabregat <36372882+mfabregat@users.noreply.github.com>
Copilot AI changed the title [WIP] Tune hyperparameters for LunarLander reinforcement learning Tune DQN hyperparameters for LunarLander-v3: achieve 235+ avg reward Dec 6, 2025
Copilot AI requested a review from mfabregat December 6, 2025 23:01
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