Agents

DynaSchedBench agents implement a small interface: reset for a new episode, read the current observation and legal actions, then return one legal action. Agents can be used from Python or through dsbx-agent run.

Discover Available Agents

dsbx-agent list-agents

Built-in names include:

Agent	Description
spt	Shortest Processing Time baseline.
random	Uniform random legal-action baseline.
pdr:<OP_RULE>:<MACHINE_RULE>	Priority dispatching rule with explicit operation and machine rules.
gp-simplegp-best	GP-based dispatching rule extracted from a fixed best-of-run expression.
ga	Genetic Algorithm online rescheduling agent.
de	Differential Evolution online rescheduling agent.
pso	Particle Swarm Optimization online rescheduling agent.
cmaes	CMA-ES action scoring agent.
sa	Simulated Annealing online rescheduling agent.
ts	Tabu Search online rescheduling agent.
moea	MOEA/D-style multi-objective online agent.
nsga2	NSGA-II multi-objective online agent.
llm-coder	LLM-assisted heuristic code generation agent.
llm-scheduler	LLM scheduling policy with cognitive prompts.

PDR Agents

PDR agents encode their behavior in the agent name:

dsbx-agent run -d runs/minimal -o runs/minimal/pdr_spt_lit -a pdr:SPT:LIT

Operation rules:

Rule	Meaning
SPT	Shortest Processing Time.
LPT	Longest Processing Time.
MWKR	Most Work Remaining.
LWKR	Least Work Remaining.
MOPNR	Most Operations Remaining.
LOPNR	Least Operations Remaining.
FIFO	First In First Out.
LIFO	Last In First Out.

Machine rules:

Rule	Meaning
LIT	Least Idle Time, usually earliest available.
LWL	Least Workload.
SPT	Shortest Processing Time on the machine.

Evolutionary And Local Search Agents

Use agent-help to show relevant options:

dsbx-agent agent-help ga
dsbx-agent agent-help de
dsbx-agent agent-help pso
dsbx-agent agent-help cmaes
dsbx-agent agent-help sa
dsbx-agent agent-help ts
dsbx-agent agent-help moea
dsbx-agent agent-help nsga2

Example:

dsbx-agent run \
  -d runs/minimal \
  -o runs/minimal/ga \
  -a ga \
  --ga-population-size 16 \
  --ga-generations 8 \
  --ga-rollout-steps 3 \
  --ga-random-seed 42

The rollout-step options control whether the agent evaluates candidate actions with a short environment rollout or with the faster one-step score estimate. Use small budgets for smoke tests and larger budgets for final experiments.

LLM Agents

LLM agents can be configured from environment variables:

export DYNA_SCHEDBENCH_LLM_PROVIDER=openai
export DYNA_SCHEDBENCH_LLM_API_KEY=your_api_key
export DYNA_SCHEDBENCH_LLM_MODEL=gpt-4o-mini

Common CLI overrides:

dsbx-agent run \
  -d runs/minimal \
  -o runs/minimal/llm_scheduler \
  -a llm-scheduler \
  --llm-provider openai \
  --llm-model gpt-4o-mini \
  --llm-temperature 0.2 \
  --llm-sched-info-level L2 \
  --llm-sched-mode direct \
  --llm-sched-max-candidates 8

LLM scheduler options group into:

• provider and transport: --llm-provider, --llm-model, --llm-base-url, --llm-timeout;

• sampling: --llm-temperature, --llm-top-p, --llm-top-k;

• prompt context: --llm-sched-info-level, --llm-sched-mode, --llm-sched-use-few-shot, --llm-sched-max-examples;

• action selection: --llm-sched-max-candidates, --llm-sched-select-machine;

• refinement: --llm-sched-refinement, --llm-sched-reflection-rounds, --llm-sched-voting-n.

LLMCoder has additional candidate-generation and sandbox-evaluation options:

dsbx-agent agent-help llm-coder

If no LLM API key is available, DynaSchedBench uses a null client or fallback heuristic behavior where implemented. This keeps local tests reproducible while making it clear in logs that no real LLM call was made.

Python Agent Interface

Custom agents can follow dsbx.Agents.Base.BaseAgent:

from typing import Any


class MyAgent:
    def reset(self, scenario_info: dict[str, Any] | None = None) -> None:
        self.num_decisions = 0

    def act(self, obs: dict[str, Any], legal_actions: list[dict[str, Any]], env: Any):
        self.num_decisions += 1
        if not legal_actions:
            return None
        return min(legal_actions, key=lambda a: a.get("process_time", 0.0))

Use the environment loop shown in Quickstart to evaluate custom agents.