Skip to main content

To do

limited intake to enforce sharing​

  • fix last outcome befopre dying

more agents-on-one-spot gird with numpy operations​

  • simplified rules, random enagagement rules if multiple agents in one cell

layered cooperationn in SocialBehavior​

  • Marl Book example

Display Maslow's Pyramid and describe project from bottom to top:

  • First layer: PredatorPreyGrass project. Typical for the first layer, physical need (eeating), survival an reproduction.
  • Second layer: social needs. Need to corporate

Dynamic training​

  • Create training algorithm of competing policies and select 'winner' after each iteration/a number of iterations. Competing policies have different environment configs. Goal: optimize environment parameters more efficiently and automatically at run time rather than manually after full (10 hour) experiments. Determine success:

    • fitness metrics
    • ability to co-adapt

  • curriculum reward tuning

  • Fitness parameters:

    • offspring per agent
    • offspring per agent per energy

  • Protocol for storing/retrieving stats per step (outside env: in evaluation loop)

Examples to try out​

Environment enhancements​

  • Male & Female reproduction instead of asexual reproduction
  • Build wall or move wall
  • Adding water/rivers

Improve network​

  •       model_config={
              "conv_filters": [
                  [16, [3, 3], 1],
                  [32, [3, 3], 1],
                  [64, [3, 3], 1],
              ],
          }

  • the network is now adjustable to the observation range. was not effectively tuned for observation_range = 9 (for Prey)

  • TODO: test experiment"_network_tuning" on HBP computer

  • change config_ppo_gpu

    Experiments​

  • Tuning hyperparameters and env parameters simultaneously (see chat)

  • max_steps_per_episode: For policy learning performance: 500–2000 steps per episode is a common sweet spot in multi-agent RL — long enough for interactions to unfold, short enough for PPO to assign credit.

    For open-ended co-evolution (your case): you might intentionally want longer episodes (e.g. 2000–5000) so emergent dynamics have time to play out, even if training is slower.

    A good trick is to curriculum the horizon:

    Start short (e.g. 500–1000) → agents learn basic survival.

    Gradually increase (e.g. +500 every N iterations) → expose them to longer ecological timescales.

    “works-in-practice” plan for your PredPreyGrass run, plus what to tweak as you lengthen episodes.

    Recommended episode horizon + hyperparameters (curriculum)​

    Start shorter for stability/throughput, then stretch to let eco-dynamics (booms, busts, Red-Queen) unfold.

    Phase A (bootstrap)

    • max_steps = 1_000
    • gamma = 0.995 (effective credit horizon ≈ 1/(1−γ) ≈ 200 steps)
    • lambda_ (GAE) = 0.95–0.97

    Phase B (mid)

    • max_steps = 2_000–3_000
    • gamma = 0.997–0.998 (horizon ≈ 333–500)
    • lambda_ = 0.96–0.97

    Phase C (long-term dynamics)

    • max_steps = 4_000–5_000
    • gamma = 0.998–0.999 (horizon ≈ 500–1 000)
    • lambda_ = 0.97

    Why that mapping? PPO’s useful credit horizon is ~1/(1−γ). As you increase max_steps, you raise γ so actions can “see” far enough ahead without making variance explode.

    Remember: in your env, if max_steps isn’t set in the config, it silently defaults to 10 000—so set it explicitly to avoid accidental long runs.

    Batch/throughput knobs to adjust as episodes get longer​

    Keep ~4–10 episodes per PPO iteration so you still get decent reset diversity:

    • train_batch_size: roughly episodes_per_iter × max_steps. Example: at max_steps=1_000, use 8_000–16_000. When you move to max_steps=3_000, bump toward 24_000–48_000.
    • rollout_fragment_length: increase with horizon so GAE has longer contiguous fragments (e.g., 200 → 400 → 800).
    • num_envs_per_env_runner: raise a bit as episodes lengthen to maintain sampler throughput.
    • KL/clip: leave defaults unless you see instability; longer horizons often benefit from slightly smaller learning rate rather than big clip/kl changes.

    When to stop stretching episodes​

    • If timing/iter_minutes balloons or TensorBoard curves update too slowly, hold the current max_steps for a while.
    • If you see extinction before the cap, longer episodes won’t help—tune ecology (e.g., energy gains/losses) instead.

    Make available the BHP archive in a repository​

    LT-goal acquire more wealth as a population​

    • Energy as a proxy of wealth
    • Only the top 10% of energy reproduces?
    • Escaping the Malthusian trap

    Integrate Dynamic Field Theory​

    • Wrapper around brain
    • Visualize first!!!

    Posting on the linkedin The Behavior Patterns Project?​

    The Malthusian Trap in a Predator–Prey Co-Evolutionary System​

    • Limit population size of predators or prey; is that beneficial compared to unbounded reproduction?
    • [2.5]: experiment_1 / experiment are powerfull examples of the Malthusian trap. Record this on site. Create a "Malthusian Trap"