gym_fishing v0.0.7

bump

gym_fishing v0.0.5

Additional models:

• Myers, May, Ricker, Beverton-Holt, Allen, ModelUncertainty, NonStationary
• refactor code and tests to conform to standards, see Makefile [#42]

gym_fishing v0.0.4

Release notes

• fix plotting method

• Add three new environments:

• fishing_v2 uses an environment that has a tipping point (as case which technically violates the concavity assumptions of Reed's 1979 proof, though the optimal solution with small noise is still essentially the same constant escapement strategy)

• fishing_v3 with observation error (also not covered by Reed's proof, and the optimal strategy is not constant escapement in this case, as we showed recently using POMDP methods: https://github.com/boettiger-lab/pomdp-intro )

• fishing_v4 adds uncertainty around the parameters. Technically I believe you can nest this case into a classic MDP framework using a Bayesian learning rule, see https://github.com/boettiger-lab/mdplearning . Curious to explore 'transfer learning' in this one as well, e.g. can it learn something more general / more robust than agents (like all the other examples) which are only ever trained on a fixed parameter set?

gym_fishing v0.0.3

• adds simulate and plotting methods
• some tweaks to the default parameter choices
• updated stable-baselines3 examples

gym_fishing

Initial release of gym_fishing: An OpenAI Gym implementation of the classic optimal fishing harvest problem, for benchmarking Deep Reinforcement Learning applications to ecological management and conservation problems.