Tech-Circle #18 Pythonではじめる強化学習 OpenAI Gym 体験ハンズオン
The document discusses applying theory of mind, or the ability to infer the intentions of other agents, to multi-agent reinforcement learning. It introduces three papers that use Bayesian reasoning to model other agents in the Hanabi game. Specifically, the papers develop methods for Bayesian action decoding and simplified action decoding to enable agents to reason about each other's intentions du
Gym
Gym is a standard API for reinforcement learning, and a diverse collection of reference environments# The Gym interface is simple, pythonic, and capable of representing general RL problems: import gym env = gym.make("LunarLander-v2", render_mode="human") observation, info = env.reset(seed=42) for _ in range(1000): action = policy(observation) # User-defined policy function observation, reward, ter
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