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games_registry.py

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+# games_registry.py
+"""Centralized registry for available games and their simulators."""
+
+from games.tic_tac_toe import get_tic_tac_toe_game
+from games.prisoners_dilemma import get_prisoners_dilemma_game
+from games.connect_four import get_connect_four_game
+from games.matrix_rps import get_matrix_rps_game
+from games.matrix_pd import get_matrix_pd_game
+from games.kuhn_poker import get_kuhn_poker_game
+from games.matching_pennies import get_matching_pennies_game
+
+from simulators.tic_tac_toe_simulator import TicTacToeSimulator
+from simulators.prisoners_dilemma_simulator import PrisonersDilemmaSimulator
+from simulators.connect_four_simulator import ConnectFourSimulator
+from simulators.matrix_game_simulator import MatrixGameSimulator
+from simulators.kuhn_poker_simulator import KuhnPokerSimulator
+from simulators.matching_pennies_simulator import MatchingPenniesSimulator
+
+# Register games here
+GAMES_REGISTRY = {
+    "tic_tac_toe": {
+        "loader": get_tic_tac_toe_game,
+        "simulator": TicTacToeSimulator,
+        "display_name": "Tic-Tac-Toe",
+    },
+    "prisoners_dilemma": {
+        "loader": get_prisoners_dilemma_game, # Iterated PD: Multi-round, emphasizing strategy development over repeated interactions.
+        "simulator": PrisonersDilemmaSimulator,
+        "display_name": "Iterated Prisoner's Dilemma",
+    },
+    "connect_four": {
+        "loader": get_connect_four_game,
+        "simulator": ConnectFourSimulator,
+        "display_name": "Connect Four",
+    },
+    "rps": {
+        "loader": get_matrix_rps_game,
+        "simulator": MatrixGameSimulator,
+        "display_name": "Rock-Paper-Scissors (Matrix)",
+    },
+    "matrix_pd": {
+        "loader": get_matrix_pd_game,  # Matrix PD: Single-round, with the payoff matrix as the primary structure.
+        "simulator": MatrixGameSimulator,
+        "display_name": "Prisoner's Dilemma (Matrix)",
+    },
+    "kuhn_poker": {
+        "loader": get_kuhn_poker_game,
+        "simulator": KuhnPokerSimulator,
+        "display_name": "Kuhn Poker",
+    },
+    "matching_pennies": {
+        "loader": get_matching_pennies_game,
+        "simulator": MatchingPenniesSimulator,
+        "display_name": "Matching Pennies (3P)",
+    },
+}

llm_registry.py

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+from transformers import pipeline
+
+# Registry of available LLMs
+# These are just examples of models that run fast enough to work in this proof of concept.
+# But they don't give us good results.
+# Ideally we would use 'intruct-finetuned' models.
+# I have tried some models and these actually worked well (i.e. followed the prompt): microsoft/Phi-3-mini-4k-instruct, Qwen/Qwen2.5-Coder-32B-Instruct, Qwen/Qwen2.5-72B-Instruct, mistralai/Mistral-7B-Instruct-v0.3,dolly-v2-3b, dolly-v2-12b
+# Note. Need to read the models documentation on how to prompt them. See example for Microsoft's Phi.
+
+
+LLM_REGISTRY = {
+    "gpt2": {
+        "display_name": "GPT-2",
+        "description": "A medium-sized transformer-based language model by OpenAI.",
+        "model_loader": lambda: pipeline("text-generation", model="gpt2"),
+    },
+    "flan_t5_small": {
+        "display_name": "FLAN-T5 Small",
+        "description": "A fine-tuned T5 model optimized for instruction-following tasks.",
+        "model_loader": lambda: pipeline("text-generation", model="google/flan-t5-small"),
+    },
+    "distilgpt2": {
+        "display_name": "DistilGPT-2",
+        "description": "A smaller and faster version of GPT-2.",
+        "model_loader": lambda: pipeline("text-generation", model="distilgpt2"),
+    },
+}

requirements.txt

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+absl-py==2.1.0
+aiofiles==23.2.1
+annotated-types==0.7.0
+anyio==4.8.0
+attrs==24.3.0
+Brotli @ file:///Users/runner/miniforge3/conda-bld/brotli-split_1725267521616/work
+certifi @ file:///home/conda/feedstock_root/build_artifacts/certifi_1734380492396/work/certifi
+cffi @ file:///Users/runner/miniforge3/conda-bld/cffi_1725560567968/work
+charset-normalizer @ file:///home/conda/feedstock_root/build_artifacts/charset-normalizer_1733218092148/work
+click==8.1.8
+contextlib2==21.6.0
+contourpy==1.3.0
+cycler==0.12.1
+exceptiongroup==1.2.2
+fastapi==0.115.6
+ffmpy==0.5.0
+filelock @ file:///home/conda/feedstock_root/build_artifacts/filelock_1733240801289/work
+fonttools==4.55.3
+fsspec==2024.12.0
+gmpy2 @ file:///Users/runner/miniforge3/conda-bld/gmpy2_1733462620396/work
+gradio==4.44.1
+gradio_client==1.3.0
+h11==0.14.0
+h2 @ file:///home/conda/feedstock_root/build_artifacts/h2_1733298745555/work
+hpack @ file:///home/conda/feedstock_root/build_artifacts/hpack_1733299205993/work
+httpcore==1.0.7
+httpx==0.28.1
+huggingface-hub==0.27.0
+hyperframe @ file:///home/conda/feedstock_root/build_artifacts/hyperframe_1733298771451/work
+idna @ file:///home/conda/feedstock_root/build_artifacts/idna_1733211830134/work
+importlib_resources==6.5.2
+Jinja2 @ file:///home/conda/feedstock_root/build_artifacts/jinja2_1734823942230/work
+kiwisolver==1.4.7
+markdown-it-py==3.0.0
+MarkupSafe==2.1.5
+matplotlib==3.9.4
+mdurl==0.1.2
+mkl-service==2.4.0
+mkl_fft==1.3.11
+mkl_random @ file:///Users/runner/miniforge3/conda-bld/mkl_random_1728790298412/work
+ml-collections==0.1.1
+mpmath @ file:///home/conda/feedstock_root/build_artifacts/mpmath_1733302684489/work
+networkx @ file:///home/conda/feedstock_root/build_artifacts/networkx_1698504735452/work
+numpy @ file:///private/var/folders/c_/qfmhj66j0tn016nkx_th4hxm0000gp/T/abs_b7iptlxgej/croot/numpy_and_numpy_base_1708638622773/work/dist/numpy-1.26.4-cp39-cp39-macosx_10_9_x86_64.whl#sha256=1ca37e4fdfd421b20423d3f5f08de21aeee8ef917ddcee6667076d3dc7937879
+open-spiel==1.5
+orjson==3.10.13
+packaging==24.2
+pandas==2.2.3
+pillow==10.4.0
+pycparser @ file:///home/conda/feedstock_root/build_artifacts/bld/rattler-build_pycparser_1733195786/work
+pydantic==2.10.4
+pydantic_core==2.27.2
+pydub==0.25.1
+Pygments==2.19.1
+pyparsing==3.2.1
+PySocks @ file:///home/conda/feedstock_root/build_artifacts/pysocks_1733217236728/work
+python-dateutil==2.9.0.post0
+python-multipart==0.0.20
+pytz==2024.2
+PyYAML @ file:///Users/runner/miniforge3/conda-bld/pyyaml_1725456176118/work
+regex==2024.11.6
+requests @ file:///home/conda/feedstock_root/build_artifacts/requests_1733217035951/work
+rich==13.9.4
+ruff==0.8.6
+safetensors==0.5.0
+scipy==1.13.1
+semantic-version==2.10.0
+shellingham==1.5.4
+six @ file:///home/conda/feedstock_root/build_artifacts/six_1733380938961/work
+sniffio==1.3.1
+starlette==0.41.3
+sympy @ file:///home/conda/feedstock_root/build_artifacts/sympy_1727529700862/work
+tokenizers==0.21.0
+tomlkit==0.12.0
+torch==2.2.2
+torchaudio==2.2.2
+torchvision==0.17.2
+tqdm==4.67.1
+transformers==4.47.1
+typer==0.15.1
+typing_extensions @ file:///home/conda/feedstock_root/build_artifacts/typing_extensions_1733188668063/work
+tzdata==2024.2
+urllib3 @ file:///home/conda/feedstock_root/build_artifacts/urllib3_1734859416348/work
+uvicorn==0.34.0
+websockets==12.0
+zipp==3.21.0
+zstandard==0.23.0