> For the complete documentation index, see [llms.txt](https://docs.macrocosmos.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.macrocosmos.ai/subnets/subnet-1-apex/subnet-1-current-competitions/ended-rl-tron-competition.md). # Ended: RL Tron Competition In this head-to-head reinforcement learning competition for Apex, miners train RL agents to play Tron on their own machines and submit their models in TorchScript (`.pt` files) for evaluation. All miners face off in a duels in a single elimination bracket-style tournament, where the winner takes emissions. ### Tron Settings * Be sure the model loads with `torch.jit.load()` before submitting. * Submissions must be in `.pt` files to be accepted. * Max submission size is 100 MB. * The model must accept an input tensor of shape `(1, 5, H, W)` and output Q-values / logits of shape `(4,)` over the action space `[UP, RIGHT, DOWN, LEFT]`. ### Round Structure A round is run as a **single-elimination bracket**. Miners can make submissions while the round is `OPEN`. Miners get one submission per hotkey - if multiple submissions are made under the same hotkey, the most recent submission is used during the evaluation phase. During the evaluation phase: * Miners are seeded into a single elimination bracket. * Every match is a head-to-head duel between two miners. * The winner advances to the next round of the bracket. * The last surviving miner is the round winner and receives all competition emissions, annealing with the burn. ### Match Structure A single match is a **duel between two miners**, consisting of several Tron games played head-to-head. The miner with the higher win rate across the games wins the match and advances in the bracket. * The default is **3 games per match**. Player spawn slots alternate every game to minimize positional advantages. * Each game is played on a **30×30 playable grid** (default) bordered 1-block walls. * Miners spawn in **opposite corners** (top-left and bottom-right) for maximum separation. * Each game runs for at most **500 ticks**. * A game ends when: * one player is alive (that player wins), * both players die on the same tick (draw), * the tick limit is reached with both still alive (draw). * Trails are permanent - riding into your own trail, the opponent's trail, or a wall kills you. * A miner that does not respond in time on a given tick is defaulted to "continue straight" - there is no per-tick penalty, but continuing straight may run the miner into a wall or trail. #### Per-Tick Miner I/O The miner runs as a long-running HTTP server inside its sandbox. The orchestrator calls the miner **every game tick** (not just at the start) with the current state. Each tick, the miner receives: * The full **grid** as a 2D array (`0`=empty, `1`=wall, `2+`=trail cells) * Its own **position** `[y, x]`, **direction** (`0`=UP, `1`=RIGHT, `2`=DOWN, `3`=LEFT), and **alive** status * The opponent's **position(s)** and **alive** status * The pre-filtered list of **valid actions** (excludes reversing direction) The miner returns a single integer action `0–3`. #### Timing * **Per-tick move timeout**: 0.1 seconds. Exceeding this defaults the miner to its current direction. * **Per-game wall-clock timeout**: 120 real-time second maximum per simulated game. ### Evaluation #### Game Score Every game produces a per-game score for each miner based on a **death-cause cascade**. Rules apply in order; the first rule that matches your situation becomes your score.
#Your situationScore
1You killed your opponent (their killed_by is you) and you're still alive1.00
2You're still alive and your opponent self-destructed (hit a wall or their own trail)0.80
3You killed your opponent but also died on the same step (head-on, mutual trail-kill, or you wall-died while your trail killed them)0.40
4Both players alive at max_steps (timeout draw)0.25
5Your opponent killed you and you did not kill them0.10
6You died alone (wall or your own trail), no kill credit0.00
* If a game fails to start due to model load failures, both miners receive 0.0 for that game. * The scoring rewards aggression: a clean kill (`1.00`) is worth more than waiting for your opponent to crash (`0.80`). #### Match Score A match's score for each miner is the **average** of per-game scores across all games in the match: ``` match_score = sum(per_game_scores) / num_games ``` So a match win rate is bounded in `[0.0, 1.0]`. #### Match Outcome The miner with the higher match score **wins the match** and advances in the bracket. A losing miner is eliminated from the bracket. Surviving miners are paired up for the next round of the bracket and play another match. This continues until one miner remains. * Information on game stats and outcomes can be found in the eval metadata. #### Aggregate Stats * **`eval_raw_score`** = the number of rounds this submission has survived. * **`eval_score = rounds_survived / total_rounds`** These numbers do not determine the bracket winner - they are tracking stats. The round winner is the **last surviving miner in the bracket**. ### Additional Details * View results on the website's [competition dashboard.](https://apex.macrocosmos.ai/competitions/8) * Miner code is revealed 2 days after evaluation. * Round length: 2 days. * 1% `raw_score` threshold to beat current top scorer. * Submission fee: $1.40 USD, converted to the current TAO price. * Logs are opened after the current round is completed. * Multiple submissions: * The rate limit is 4 submissions per hotkey within 24 hours, across all competitions. * A guide on training a baseline model can be found in the [`train` folder](https://github.com/macrocosm-os/apex/tree/main/shared/competition/src/competition/tron/train). * Information on the RL Tron Player API can be found in [launch\_tron\_rl.py](https://github.com/macrocosm-os/apex/blob/main/shared/competition/src/competition/tron/launch_tron_rl.py). * See `requirements.txt` for information on allowed packages. * All matches produce a replay file (per-game grid history and tick-by-tick actions).