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Episodic Novelty Through Temporal Distance (ICLR 2025)

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# About

This repository implements **Episodic Novelty Through Temporal Distance(ETD)**, an exploration method for reinforcement learning that has been found to be particularly effective in Contextual MDPs(CMDP). More details can be found in the original paper([ICLR 2025 Accepted](https://openreview.net/pdf?id=I7DeajDEx7)). In case you are mainly interested in the implementation of ETD, its major components can be found at `src/algo/intrinsic_rewards/tdd.py.` We also provide a reproduction of the temporal distance learning in maze, please refer to `maze/train_cmd1.py`.

# Install

### Basic Installation

```bash
conda create -n etd python=3.9
conda activate etd
pip install -r requirements.txt
```

### Miniworld

```bash
git submodule init
git submodule update
cd src/env/gym_miniworld
pip install pyglet==1.5.11
pip install -e .
```

### Usage

### Reproduce Temporal Distance Learning in Maze

```bash
cd maze
python train_cmd1.py
```

### Train ETD on MiniGrid

Run the below command in the root directory of this repository to train a ETD agent in the standard *DoorKey-8x8* (MiniGrid) environment.

```bash
PYTHONPATH=./ python3 src/train.py \\
  --int_rew_source=TDD \\
  --env_source=minigrid \\
  --game_name=DoorKey-8x8 \\
  --int_rew_coef=1e-2
```

We also provide scripts`(*.sh)` to run the experiments of our method. The hyperparameter setup can be found in our paper.

### Citation
If you find this repo useful, please cite our paper:

````
@inproceedings{
jiang2025episodic,
title={Episodic Novelty Through Temporal Distance},
author={Yuhua Jiang and Qihan Liu and Yiqin Yang and Xiaoteng Ma and Dianyu Zhong and Hao Hu and Jun Yang and Bin Liang and Bo XU and Chongjie Zhang and Qianchuan Zhao},
booktitle={The Thirteenth International Conference on Learning Representations},
year={2025},
url={https://openreview.net/forum?id=I7DeajDEx7}
}

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